{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LERdAinRBcI8"
      },
      "source": [
        "# Sentiment Analysis with Hugging Face"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAMk-5YCBcJA"
      },
      "source": [
        "Hugging Face is an open-source and platform provider of machine learning technologies. You can use install their package to access some interesting pre-built models to use them directly or to fine-tune (retrain it on your dataset leveraging the prior knowledge coming with the first training), then host your trained models on the platform, so that you may use them later on other devices and apps.\n",
        "\n",
        "Please, [go to the website and sign-in](https://huggingface.co/) to access all the features of the platform.\n",
        "\n",
        "[Read more about Text classification with Hugging Face](https://huggingface.co/tasks/text-classification)\n",
        "\n",
        "The Hugging face models are Deep Learning based, so will need a lot of computational GPU power to train them. Please use [Colab](https://colab.research.google.com/) to do it, or your other GPU cloud provider, or a local machine having NVIDIA GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njYLvWvyBcJD"
      },
      "source": [
        "## Application of Hugging Face Text classification model Fune-tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGym_s8IBcJD"
      },
      "source": [
        "Find below a simple example, with just `3 epochs of fine-tuning`.\n",
        "\n",
        "Read more about the fine-tuning concept : [here](https://deeplizard.com/learn/video/5T-iXNNiwIs#:~:text=Fine%2Dtuning%20is%20a%20way,perform%20a%20second%20similar%20task.)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variable definition:\n",
        "\n",
        "tweet_id: Unique identifier of the tweet\n",
        "safe_tweet: Text contained in the tweet. Some sensitive information has been removed like usernames and urls\n",
        "label: Sentiment of the tweet (-1 for negative, 0 for neutral, 1 for positive)\n",
        "agreement: The tweets were labeled by three people. Agreement indicates the percentage of the three reviewers that agreed on the given label. You may use this column in your training, but agreement data will not be shared for the test set."
      ],
      "metadata": {
        "id": "a0OXD7gMtrXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install nfx\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gq3zq_oq6DG4",
        "outputId": "8742b239-3a6d-4daf-cd7c-056fb89c4c79"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nfx in /usr/local/lib/python3.10/dist-packages (0.0.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hugging face hub\n",
        "!pip install huggingface_hub\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGPYKHBeaNaB",
        "outputId": "744c3220-489a-40f8-f16f-84eb5ba785c0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.17.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install neattext\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bun7WdO478iU",
        "outputId": "878e92f7-2e04-40b3-d583-d80f371cbdb6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: neattext in /usr/local/lib/python3.10/dist-packages (0.1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install datasets\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MowFrlJ3DJmy",
        "outputId": "b003d29d-8577-4ac5-9a91-9bd2e608d0d0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.5)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.3.0)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n"
      ],
      "metadata": {
        "id": "aujrxNUae5rF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "799d8024-bd4e-4e37-d7c3-27bb3e0b42f0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.33.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "!pip install transformers[torch]\n",
        "!pip install accelerate -U\n",
        "!pip install -qU transformers datasets accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gq-up5jA7iGL",
        "outputId": "53129e6b-bb98-4e6c-f371-4d56156d6690"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.33.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.17.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZcnoZTdBBcJF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bad9e4a3-0fb1-40a4-e0eb-d0e587c2a58a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "#Import hugging face logging in\n",
        "from huggingface_hub import notebook_login\n",
        "#Dataset\n",
        "from datasets import load_dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "#Visualization Libraries\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# To extract hashtags\n",
        "import neattext.functions as nfx\n",
        "import re\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "# Download stopwords - Stopwords are commonly used words \"a\", \"the\" , \"an\", \"is\", \"are\".\n",
        "# Are removed since they dont carry significant meaning to the words\n",
        "\n",
        "# import specific functions and classes from NLTK (Natural Language Toolkit library)\n",
        "\n",
        "from nltk.tokenize import word_tokenize  # used for tokenizing text into individual words\n",
        "from nltk.corpus import stopwords # provides a list of common words that are often removed from text\n",
        "from nltk.stem import PorterStemmer # is a stemming algorithm that reduces words to their base or root form\n",
        "\n",
        "# Initializes stop variable, assigns it the list of English stopwords from the NLTK corpus.\n",
        "\n",
        "import nltk\n",
        "nltk.download(\"stopwords\")\n",
        "# creates an instance of the PorterStemmer class, assigns it to the variable stemmer.\n",
        "# The stemmer will be used later to perform word stemming, which reduces words to their base or root\n",
        "stemmer = PorterStemmer()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()\n",
        "\n"
      ],
      "metadata": {
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        "outputId": "4e2459c1-f688-456f-ae16-de33e9fd5d02"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install download"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSpH3ZWqaKJy",
        "outputId": "446f58e7-1a1b-4162-e10d-a8e9f4fc0b91"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting download\n",
            "  Downloading download-0.3.5-py3-none-any.whl (8.8 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from download) (4.66.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from download) (1.16.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from download) (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->download) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->download) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->download) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->download) (2023.7.22)\n",
            "Installing collected packages: download\n",
            "Successfully installed download-0.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "54nFq5DVBcJI"
      },
      "outputs": [],
      "source": [
        "# Disabe W&B\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "l-P3dKDjBcJJ"
      },
      "outputs": [],
      "source": [
        "# Load the dataset and display some values\n",
        "Link = 'https://raw.githubusercontent.com/Newton23-nk/Covid_Vaccine_Sentiment_Analysis/main/Datasets/Train.csv'\n",
        "df = pd.read_csv(Link)\n",
        "\n",
        "# A way to eliminate rows containing NaN values\n",
        "df = df[~df.isna().any(axis=1)]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "PQpRsVClL8G1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nwnaWsiSEnhO",
        "outputId": "7719f734-8322-47cf-b343-af2d66e83cad"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   tweet_id                                          safe_text  label  \\\n",
              "0  CL1KWCMY  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...    0.0   \n",
              "1  E3303EME  I'm 100% thinking of devoting my career to pro...    1.0   \n",
              "2  M4IVFSMS  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   -1.0   \n",
              "3  1DR6ROZ4  I mean if they immunize my kid with something ...   -1.0   \n",
              "4  J77ENIIE  Thanks to <user> Catch me performing at La Nui...    0.0   \n",
              "\n",
              "   agreement  \n",
              "0        1.0  \n",
              "1        1.0  \n",
              "2        1.0  \n",
              "3        1.0  \n",
              "4        1.0  "
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    width: 32px;\n",
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              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-280ec686-46b4-492f-9f39-bf41a0b0660d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We look at the number of positive, negative and neutral reviews\n",
        "df.label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7wE5vo-MDov",
        "outputId": "435452ca-de35-4f04-c3d7-7c2b483110c4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0.0    4908\n",
              " 1.0    4053\n",
              "-1.0    1038\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The count of the agrremtns\n",
        "df.agreement.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHCFSdIFwaUH",
        "outputId": "983f1637-581a-48f1-e63d-a248f6586984"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.000000    5866\n",
              "0.666667    3894\n",
              "0.333333     239\n",
              "Name: agreement, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Legnth of the reviews\n",
        "review_legnth = df.safe_text.str.len()\n"
      ],
      "metadata": {
        "id": "VOPPswzzMDyW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Legnth of the longest review\n",
        "max(review_legnth)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FltHVxdFMD1V",
        "outputId": "59b3fff8-3700-491b-c2cd-72fba63dc665"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "153"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Legnth of the shortest review\n",
        "min(review_legnth)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evUifWXAMD4S",
        "outputId": "43b293eb-825f-407a-95c9-19440a907267"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Length of Tweets\n",
        "text_length = df['safe_text'].apply(len)\n",
        "sns.histplot(text_length, kde=True)\n",
        "plt.title('Distribution of Text Lengths')\n",
        "plt.xlabel('Text Length')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "sxXPpLMGzk3P",
        "outputId": "51c8f379-6bd2-4a2b-8036-1c6428e468a5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The highest text length observed is 153 characters, while the minimum text length is 3 characters."
      ],
      "metadata": {
        "id": "TQBJ4qFC6xpn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of Sentiments\n",
        "sns.countplot(x='label', data=df)\n",
        "plt.title('Distribution of Sentiments')\n",
        "plt.xlabel('Sentiment Label')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kadSApR4uVcM",
        "outputId": "43257e9d-b0ee-49b7-9afd-11203d8afbf9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The distribution of sentiments in the dataset, as depicted by the count plot, shows the prevalence of different sentiment labels within the Twitter posts related to COVID-19 vaccinations.\n",
        "* Sentiment Label 0 (Neutral):\n",
        "The sentiment label \"0\" (neutral) has the highest count, with approximately 5000 instances. This suggests that a significant portion of the collected tweets exhibit a neutral sentiment when it comes to discussing COVID-19 vaccinations. Neutral sentiments often indicate that the tweets may not strongly express positive or negative opinions but rather present factual information or observations.\n",
        "\n",
        "* Sentiment Label 1 (Positive):\n",
        "The sentiment label \"1\" (positive) follows with around 4000 instances. This indicates that a substantial number of tweets show a positive sentiment towards COVID-19 vaccinations. These tweets might express support for vaccinations, share positive experiences, or provide information about vaccination availability and benefits.\n",
        "\n",
        "* Sentiment Label -1 (Negative):\n",
        "The sentiment label \"-1\" (negative) has the lowest count, with approximately 1000 instances. This suggests that a relatively smaller portion of the collected tweets exhibit a negative sentiment towards COVID-19 vaccinations. Negative sentiments can encompass concerns, skepticism, or criticism about the vaccines, their safety, or potential side effects."
      ],
      "metadata": {
        "id": "wKAQLOMvvWmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of Agreement Percentages\n",
        "plt.hist(df['agreement'], color='blue')\n",
        "plt.title('Distribution of Agreement Percentages')\n",
        "plt.xlabel('Agreement Percentage')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3AnyJEEdv2lt",
        "outputId": "b079e980-48b5-43ac-f74c-cb19b956f7d4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* From the distribution plot, it is clear that the majority of tweets have an agreement percentage of 1.000000 (100% agreement among reviewers).\n",
        "This means that for a significant portion of tweets, all three reviewers assigned the same sentiment label without disagreement.\n",
        "* For a substantial number of tweets, two out of three reviewers agreed on the assigned sentiment label with a count of 3894.\n",
        "* Finally, a smaller number of tweets have an agreement percentage of 0.333333, indicating that only one out of three reviewers agreed on the label."
      ],
      "metadata": {
        "id": "Eix9O34kxqC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate all text from the 'safe_text' column into a single string\n",
        "text = ' '.join(df['safe_text'])\n",
        "\n",
        "# Generate the word cloud with a white background\n",
        "cloud_two_cities = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
        "\n",
        "# Display the word cloud\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.imshow(cloud_two_cities, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.tight_layout(pad=1)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "M-weJwcIMD-Q",
        "outputId": "97a17493-7765-47b5-b13b-fe93b65e71b4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The high frequency of \"vaccine\" and \"vaccinate\" aligns with the overarching theme of COVID-19 vaccinations. Neutral sentiment tweets may contain factual information, discussions, or updates related to the vaccines, contributing to a neutral tone.\n",
        "* The term \"measles\" appearing prominently suggests that discussions within the neutral sentiment category often include references to the measles virus. It's possible that some tweets are drawing comparisons or discussing related topics in the context of COVID-19 vaccinations.\n",
        "* The appearance of \"kid\" and \"children\" indicates that discussions involving younger individuals, possibly in the context of vaccination decisions for children, are present within the neutral sentiment tweets."
      ],
      "metadata": {
        "id": "GGmq5kuctu2R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "IvI0NIkeoH6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUU-9_ffMEEJ",
        "outputId": "8a7feb6d-e114-4c35-967b-2f6123ae6c4e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tweet_id     0\n",
              "safe_text    0\n",
              "label        0\n",
              "agreement    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fS4Pdrz4MEMt",
        "outputId": "5355d806-7d61-4936-af8e-bd1dfea1c472"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will extract hashtags and which can also used for analysis like which was the common aside from #Covid #Vaccine"
      ],
      "metadata": {
        "id": "P-uDjuzA5ocX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get hashtags\n",
        "df['extract_hashtags'] = df['safe_text'].apply(nfx.extract_hashtags)\n",
        "df[['extract_hashtags','safe_text']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "btNqmzfRuwkw",
        "outputId": "af6320fd-8e6d-4c6a-d3bc-c1486172e43d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                extract_hashtags  \\\n",
              "0      [#MEANBOY, #MB, #MBS, #MMR, #STEGMANLIFE]   \n",
              "1                                             []   \n",
              "2                            [#whatcausesautism]   \n",
              "3                                             []   \n",
              "4                            [#jennifair, #mmr…]   \n",
              "...                                          ...   \n",
              "9996                             [#vaccineswork]   \n",
              "9997                                          []   \n",
              "9998                                          []   \n",
              "9999                                          []   \n",
              "10000                                         []   \n",
              "\n",
              "                                               safe_text  \n",
              "0      Me &amp; The Big Homie meanboy3000 #MEANBOY #M...  \n",
              "1      I'm 100% thinking of devoting my career to pro...  \n",
              "2      #whatcausesautism VACCINES, DO NOT VACCINATE Y...  \n",
              "3      I mean if they immunize my kid with something ...  \n",
              "4      Thanks to <user> Catch me performing at La Nui...  \n",
              "...                                                  ...  \n",
              "9996   Living in a time where the sperm I used to was...  \n",
              "9997   <user> <user>  In spite of all measles outbrea...  \n",
              "9998   Interesting trends in child immunization in Ok...  \n",
              "9999   CDC Says Measles Are At Highest Levels In Deca...  \n",
              "10000  Pneumonia vaccine: for women w risk of pulmona...  \n",
              "\n",
              "[9999 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0a91c7c-589d-42c3-89a9-0eeb0e3a7e4c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>extract_hashtags</th>\n",
              "      <th>safe_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[#MEANBOY, #MB, #MBS, #MMR, #STEGMANLIFE]</td>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000 #MEANBOY #M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[]</td>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[#whatcausesautism]</td>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[]</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[#jennifair, #mmr…]</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>[#vaccineswork]</td>\n",
              "      <td>Living in a time where the sperm I used to was...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>[]</td>\n",
              "      <td>&lt;user&gt; &lt;user&gt;  In spite of all measles outbrea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>[]</td>\n",
              "      <td>Interesting trends in child immunization in Ok...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>[]</td>\n",
              "      <td>CDC Says Measles Are At Highest Levels In Deca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10000</th>\n",
              "      <td>[]</td>\n",
              "      <td>Pneumonia vaccine: for women w risk of pulmona...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9999 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0a91c7c-589d-42c3-89a9-0eeb0e3a7e4c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c0a91c7c-589d-42c3-89a9-0eeb0e3a7e4c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c0a91c7c-589d-42c3-89a9-0eeb0e3a7e4c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-957d6cfd-4756-4264-b22b-7f23ee8443db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-957d6cfd-4756-4264-b22b-7f23ee8443db')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-957d6cfd-4756-4264-b22b-7f23ee8443db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove hashtags from the column and save the cleaned text to clean text column\n",
        "df['clean_text'] = df['safe_text'].apply(nfx.remove_hashtags)\n",
        "\n",
        "# preview\n",
        "df[['safe_text','clean_text']].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qmFJWyDMuwn2",
        "outputId": "cfa9188f-1e04-45a3-9c9c-8874541c689c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           safe_text  \\\n",
              "0  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...   \n",
              "1  I'm 100% thinking of devoting my career to pro...   \n",
              "2  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   \n",
              "3  I mean if they immunize my kid with something ...   \n",
              "4  Thanks to <user> Catch me performing at La Nui...   \n",
              "5  <user> a nearly 67 year old study when mental ...   \n",
              "6  Study of more than 95,000 kids finds no link b...   \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS   \n",
              "8  Coughing extra on the shuttle and everyone thi...   \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...   \n",
              "\n",
              "                                          clean_text  \n",
              "0  Me &amp; The Big Homie meanboy3000           @...  \n",
              "1  I'm 100% thinking of devoting my career to pro...  \n",
              "2              VACCINES, DO NOT VACCINATE YOUR CHILD  \n",
              "3  I mean if they immunize my kid with something ...  \n",
              "4  Thanks to <user> Catch me performing at La Nui...  \n",
              "5  <user> a nearly 67 year old study when mental ...  \n",
              "6  Study of more than 95,000 kids finds no link b...  \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS  \n",
              "8  Coughing extra on the shuttle and everyone thi...  \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-03a39135-7b25-4052-a43d-297e808bd3d2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>safe_text</th>\n",
              "      <th>clean_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000 #MEANBOY #M...</td>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000           @...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "      <td>VACCINES, DO NOT VACCINATE YOUR CHILD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03a39135-7b25-4052-a43d-297e808bd3d2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-03a39135-7b25-4052-a43d-297e808bd3d2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-03a39135-7b25-4052-a43d-297e808bd3d2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f8071d9f-8a9a-4e82-952a-418ca94b104c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8071d9f-8a9a-4e82-952a-418ca94b104c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f8071d9f-8a9a-4e82-952a-418ca94b104c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will remove the user handles and the \"RT\" retweet indicator using the nfx.remove_userhandles in the nfx library."
      ],
      "metadata": {
        "id": "V6Uo0_-RJp3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove RT and user handles\n",
        "def removeRT(text):\n",
        "    return text.replace(\"RT\" , \"\")\n",
        "df['clean_text'] = df['clean_text'].apply(lambda x: nfx.remove_userhandles(x))\n",
        "df['clean_text'] = df['clean_text'].apply(removeRT)\n",
        "#Preview of the safe text and clean text\n",
        "df[['safe_text','clean_text']].head(10)\n"
      ],
      "metadata": {
        "id": "pUmLQSlFuwqs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "7b2371b3-b65f-4be3-8c6f-b7703e1414db"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           safe_text  \\\n",
              "0  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...   \n",
              "1  I'm 100% thinking of devoting my career to pro...   \n",
              "2  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   \n",
              "3  I mean if they immunize my kid with something ...   \n",
              "4  Thanks to <user> Catch me performing at La Nui...   \n",
              "5  <user> a nearly 67 year old study when mental ...   \n",
              "6  Study of more than 95,000 kids finds no link b...   \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS   \n",
              "8  Coughing extra on the shuttle and everyone thi...   \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...   \n",
              "\n",
              "                                          clean_text  \n",
              "0  Me &amp; The Big Homie meanboy3000           @...  \n",
              "1  I'm 100% thinking of devoting my career to pro...  \n",
              "2              VACCINES, DO NOT VACCINATE YOUR CHILD  \n",
              "3  I mean if they immunize my kid with something ...  \n",
              "4  Thanks to <user> Catch me performing at La Nui...  \n",
              "5  <user> a nearly 67 year old study when mental ...  \n",
              "6  Study of more than 95,000 kids finds no link b...  \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS  \n",
              "8  Coughing extra on the shuttle and everyone thi...  \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...  "
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              "</style>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "      <td>VACCINES, DO NOT VACCINATE YOUR CHILD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-73579043-85fd-434e-b4f7-21f8254c0e5d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-73579043-85fd-434e-b4f7-21f8254c0e5d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-89d9aac4-9f06-440e-ae8d-4a291467b065 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then remove multiple spaces and strip any leading or trailing spaces using the nfx.remove_multiple_spaces function and a custom function stripSpace."
      ],
      "metadata": {
        "id": "wyC6S_GIKact"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove multiple white spaces\n",
        "def stripSpace(text):\n",
        "    return text.strip()\n",
        "df['clean_text'] = df['clean_text'].apply(nfx.remove_multiple_spaces)\n",
        "df['clean_text'] = df['clean_text'].apply(stripSpace)"
      ],
      "metadata": {
        "id": "I_7lmr0Juwtr"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To further reduce noise in the data and to remove irrelevant content, we will remove URLs from the data"
      ],
      "metadata": {
        "id": "WnPNXKZ8K3mZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove all urls\n",
        "df['clean_text'] = df['clean_text'].apply(nfx.remove_urls)\n",
        "\n",
        "df[['safe_text','clean_text']].head(10)"
      ],
      "metadata": {
        "id": "E7WqamAFuwxn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "cee69997-638c-40df-be00-69f8425255a7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           safe_text  \\\n",
              "0  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...   \n",
              "1  I'm 100% thinking of devoting my career to pro...   \n",
              "2  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   \n",
              "3  I mean if they immunize my kid with something ...   \n",
              "4  Thanks to <user> Catch me performing at La Nui...   \n",
              "5  <user> a nearly 67 year old study when mental ...   \n",
              "6  Study of more than 95,000 kids finds no link b...   \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS   \n",
              "8  Coughing extra on the shuttle and everyone thi...   \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...   \n",
              "\n",
              "                                          clean_text  \n",
              "0  Me &amp; The Big Homie meanboy3000 @ Stegman S...  \n",
              "1  I'm 100% thinking of devoting my career to pro...  \n",
              "2              VACCINES, DO NOT VACCINATE YOUR CHILD  \n",
              "3  I mean if they immunize my kid with something ...  \n",
              "4  Thanks to <user> Catch me performing at La Nui...  \n",
              "5  <user> a nearly 67 year old study when mental ...  \n",
              "6  Study of more than 95,000 kids finds no link b...  \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS  \n",
              "8  Coughing extra on the shuttle and everyone thi...  \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...  "
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              "      <th>0</th>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000 #MEANBOY #M...</td>\n",
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              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "      <td>VACCINES, DO NOT VACCINATE YOUR CHILD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4642e8ac-894b-4212-948e-077ccb9ecbac')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4642e8ac-894b-4212-948e-077ccb9ecbac button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4642e8ac-894b-4212-948e-077ccb9ecbac');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ef646b03-4f53-4ffb-b57a-41ffb3559721\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef646b03-4f53-4ffb-b57a-41ffb3559721')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef646b03-4f53-4ffb-b57a-41ffb3559721 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will remove punctuation to standardize the data and to ensure consistency in the data"
      ],
      "metadata": {
        "id": "IntjVdNWLmap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove pucntuations\n",
        "df['clean_text'] = df['clean_text'].apply(nfx.remove_puncts)\n",
        "\n",
        "df[['safe_text','clean_text']].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "oYJb2TWfAFTj",
        "outputId": "1bae3ce0-e77f-4cf0-e206-ff9c1bc51b4f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           safe_text  \\\n",
              "0  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...   \n",
              "1  I'm 100% thinking of devoting my career to pro...   \n",
              "2  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   \n",
              "3  I mean if they immunize my kid with something ...   \n",
              "4  Thanks to <user> Catch me performing at La Nui...   \n",
              "5  <user> a nearly 67 year old study when mental ...   \n",
              "6  Study of more than 95,000 kids finds no link b...   \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS   \n",
              "8  Coughing extra on the shuttle and everyone thi...   \n",
              "9  AIDS vaccine created at Oregon Health &amp; Sc...   \n",
              "\n",
              "                                          clean_text  \n",
              "0  Me amp The Big Homie meanboy3000 @ Stegman St ...  \n",
              "1  Im 100% thinking of devoting my career to prov...  \n",
              "2               VACCINES DO NOT VACCINATE YOUR CHILD  \n",
              "3  I mean if they immunize my kid with something ...  \n",
              "4  Thanks to <user> Catch me performing at La Nui...  \n",
              "5  <user> a nearly 67 year old study when mental ...  \n",
              "6  Study of more than 95000 kids finds no link be...  \n",
              "7                   psa: VACCINATE YOUR FUCKING KIDS  \n",
              "8  Coughing extra on the shuttle and everyone thi...  \n",
              "9  AIDS vaccine created at Oregon Health amp Scie...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-54856f2c-2ec6-4ee5-8919-732a33f7e0be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>safe_text</th>\n",
              "      <th>clean_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000 #MEANBOY #M...</td>\n",
              "      <td>Me amp The Big Homie meanboy3000 @ Stegman St ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "      <td>Im 100% thinking of devoting my career to prov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "      <td>VACCINES DO NOT VACCINATE YOUR CHILD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "      <td>&lt;user&gt; a nearly 67 year old study when mental ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Study of more than 95,000 kids finds no link b...</td>\n",
              "      <td>Study of more than 95000 kids finds no link be...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "      <td>psa: VACCINATE YOUR FUCKING KIDS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "      <td>Coughing extra on the shuttle and everyone thi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>AIDS vaccine created at Oregon Health &amp;amp; Sc...</td>\n",
              "      <td>AIDS vaccine created at Oregon Health amp Scie...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54856f2c-2ec6-4ee5-8919-732a33f7e0be')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-54856f2c-2ec6-4ee5-8919-732a33f7e0be button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-54856f2c-2ec6-4ee5-8919-732a33f7e0be');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-7a6e516a-069d-4d85-9660-0de703d5714a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a6e516a-069d-4d85-9660-0de703d5714a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a6e516a-069d-4d85-9660-0de703d5714a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets check on null text, some might be cleaned everything\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJ52lz7cAFbT",
        "outputId": "db2f8d48-9008-4e04-c807-11fd60ca7c0c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tweet_id            0\n",
              "safe_text           0\n",
              "label               0\n",
              "agreement           0\n",
              "extract_hashtags    0\n",
              "clean_text          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then remove punctuation from each hashtag and also remove the '#' symbol. This is to standardize hashtag representations."
      ],
      "metadata": {
        "id": "ODVE0ZELMfa3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lets get hashtags into a good string and remove the hashes beside the tag\n",
        "def clean_hash_tag(text):\n",
        "    return \" \".join([nfx.remove_puncts(x).replace(\"#\", \"\") for x in text])\n",
        "\n",
        "df['extract_hashtags'] = df['extract_hashtags'].apply(clean_hash_tag)"
      ],
      "metadata": {
        "id": "KJZUvTZiA7Nx"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dealing with Emojis"
      ],
      "metadata": {
        "id": "ijabTkoseUyt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dealing with emojis\n",
        "df['clean_text'].apply(nfx.extract_emojis)"
      ],
      "metadata": {
        "id": "ZeD_n33ElN5V",
        "outputId": "98700e4f-f8fc-4be5-cb70-886f604865e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        []\n",
              "1        []\n",
              "2        []\n",
              "3        []\n",
              "4        []\n",
              "         ..\n",
              "9996     []\n",
              "9997     []\n",
              "9998     []\n",
              "9999     []\n",
              "10000    []\n",
              "Name: clean_text, Length: 9999, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing the emojis\n",
        "df['clean_text'] = df['clean_text'].apply(nfx.remove_emojis)"
      ],
      "metadata": {
        "id": "zxf347jMA7T1"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace '<user>' with an empty string in the 'clean_text' column\n",
        "df['clean_text'] = df['clean_text'].str.replace('<user>', '')\n",
        "df['clean_text'] = df['clean_text'].str.replace('@', '')\n",
        "df['clean_text'] = df['clean_text'].str.replace('<url>', '')\n",
        "df['clean_text'] = df['clean_text'].str.replace('measles', 'Measles')\n",
        "df['clean_text'] = df['clean_text'].str.replace('“', '')\n"
      ],
      "metadata": {
        "id": "toTsAES5PcGa"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* * Remove ['vaccine', 'vaccines', 'vaccinate', 'vaccinated', 'vaccinations', 'vaccination'] to ['vaccine'] **"
      ],
      "metadata": {
        "id": "qCvyc8R2lV9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We define the words to replace\n",
        "words_to_replace = ['vaccine', 'vaccines', 'vaccinate', 'vaccinated', 'vaccinations', 'vaccination']\n",
        "\n",
        "# Pattern to match any of the words in the list, using a regular expression\n",
        "pattern = r'\\b(?:{})\\b'.format('|'.join(words_to_replace))\n",
        "\n",
        "# Function to replace the words with 'vaccine'\n",
        "def replace_with_vaccine(text):\n",
        "    return text.str.replace(pattern, 'vaccine', case=False)\n",
        "\n",
        "# Apply the function to the 'safe_text' column\n",
        "df['clean_text'] = replace_with_vaccine(df['clean_text'])"
      ],
      "metadata": {
        "id": "mM3Dmn7BAFfc"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4.8 Replace ['kids', 'child', 'children'] to ['child']**"
      ],
      "metadata": {
        "id": "A4m4FLhNl9-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "words_to_replace_2 = ['kids', 'child', 'children']\n",
        "\n",
        "# Pattern to match any of the words in the list, using a regular expression\n",
        "pattern_2  = r'\\b(?:{})\\b'.format('|'.join(words_to_replace_2 ))\n",
        "\n",
        "# Function to replace the words with 'vaccine'\n",
        "def replace_with_child (text):\n",
        "    return text.str.replace(pattern_2 , 'child', case=False)\n",
        "\n",
        "# Apply the function to the 'safe_text' column\n",
        "df['clean_text'] = replace_with_child(df['clean_text'])"
      ],
      "metadata": {
        "id": "EFs_x11og21H"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk\n",
        "import nltk"
      ],
      "metadata": {
        "id": "dB8GxAnWmcpd",
        "outputId": "11a1a254-f4b7-40b8-9900-bf6c7513b67a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.6.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_ = nltk.FreqDist(df['clean_text'].str.split().sum())\n",
        "words = words_.most_common(30)\n",
        "\n",
        "words"
      ],
      "metadata": {
        "id": "15NnFecEqioI",
        "outputId": "42abbd10-4961-475b-b604-ba916f7647f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('vaccine', 4902),\n",
              " ('to', 3435),\n",
              " ('the', 3413),\n",
              " ('Measles', 3022),\n",
              " ('child', 2487),\n",
              " ('of', 2212),\n",
              " ('a', 2142),\n",
              " ('in', 1926),\n",
              " ('and', 1837),\n",
              " ('I', 1635),\n",
              " ('is', 1501),\n",
              " ('for', 1489),\n",
              " ('you', 1019),\n",
              " ('your', 950),\n",
              " ('are', 925),\n",
              " ('that', 908),\n",
              " ('have', 902),\n",
              " ('not', 820),\n",
              " ('on', 788),\n",
              " ('their', 728),\n",
              " ('get', 678),\n",
              " ('be', 669),\n",
              " ('at', 668),\n",
              " ('my', 667),\n",
              " ('with', 665),\n",
              " ('The', 634),\n",
              " ('from', 627),\n",
              " ('this', 612),\n",
              " ('who', 609),\n",
              " ('it', 607)]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing Stop words"
      ],
      "metadata": {
        "id": "cQrdMs_1Y1IR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\",\".join(stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "StQYFiMxY7VJ",
        "outputId": "e06da802-5ff3-4483-f6c0-3bae7bfa13b5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"i,me,my,myself,we,our,ours,ourselves,you,you're,you've,you'll,you'd,your,yours,yourself,yourselves,he,him,his,himself,she,she's,her,hers,herself,it,it's,its,itself,they,them,their,theirs,themselves,what,which,who,whom,this,that,that'll,these,those,am,is,are,was,were,be,been,being,have,has,had,having,do,does,did,doing,a,an,the,and,but,if,or,because,as,until,while,of,at,by,for,with,about,against,between,into,through,during,before,after,above,below,to,from,up,down,in,out,on,off,over,under,again,further,then,once,here,there,when,where,why,how,all,any,both,each,few,more,most,other,some,such,no,nor,not,only,own,same,so,than,too,very,s,t,can,will,just,don,don't,should,should've,now,d,ll,m,o,re,ve,y,ain,aren,aren't,couldn,couldn't,didn,didn't,doesn,doesn't,hadn,hadn't,hasn,hasn't,haven,haven't,isn,isn't,ma,mightn,mightn't,mustn,mustn't,needn,needn't,shan,shan't,shouldn,shouldn't,wasn,wasn't,weren,weren't,won,won't,wouldn,wouldn't\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = set(stopwords.words('english'))"
      ],
      "metadata": {
        "id": "awC5bscPY7eN"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert safe_text column to lower so as to apply stop words\n",
        "\n",
        "df['clean_text'] = df['clean_text'].str.lower()"
      ],
      "metadata": {
        "id": "bEJRhs22Y7iT"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove stop words\n",
        "def remove_stop (x):\n",
        "  return \",\".join([word for word in str(x).split() if word not in stop_words])\n",
        "\n",
        "\n",
        "df['clean_text'] = df['clean_text'].apply(lambda x : remove_stop(x) )"
      ],
      "metadata": {
        "id": "mGAA1ZZzrjgj"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To remove punctuations\n",
        "df['clean_text'] = df['safe_text'].str.replace(r\"[&;. ,#@\\\"!']\", \" \", regex=True)\n"
      ],
      "metadata": {
        "id": "co8Fvcf9qirV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Use Stemmetizaton"
      ],
      "metadata": {
        "id": "R_67XM10sJdX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ps = PorterStemmer()"
      ],
      "metadata": {
        "id": "diyqeTObsiME"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final = []\n",
        "for word in df['clean_text']:\n",
        "   final.append(ps.stem(word))\n",
        "   final.append(\" \")"
      ],
      "metadata": {
        "id": "XG9jHqV3siqf"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "ib2phWW_si0X",
        "outputId": "dcf83122-eb72-4e6c-e303-5eab530dbf29"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   tweet_id                                          safe_text  label  \\\n",
              "0  CL1KWCMY  Me &amp; The Big Homie meanboy3000 #MEANBOY #M...    0.0   \n",
              "1  E3303EME  I'm 100% thinking of devoting my career to pro...    1.0   \n",
              "2  M4IVFSMS  #whatcausesautism VACCINES, DO NOT VACCINATE Y...   -1.0   \n",
              "3  1DR6ROZ4  I mean if they immunize my kid with something ...   -1.0   \n",
              "4  J77ENIIE  Thanks to <user> Catch me performing at La Nui...    0.0   \n",
              "\n",
              "   agreement                extract_hashtags  \\\n",
              "0        1.0  MEANBOY MB MBS MMR STEGMANLIFE   \n",
              "1        1.0                                   \n",
              "2        1.0                whatcausesautism   \n",
              "3        1.0                                   \n",
              "4        1.0                  jennifair mmr…   \n",
              "\n",
              "                                          clean_text  \n",
              "0  Me  amp  The Big Homie meanboy3000  MEANBOY  M...  \n",
              "1  I m 100% thinking of devoting my career to pro...  \n",
              "2   whatcausesautism VACCINES  DO NOT VACCINATE Y...  \n",
              "3  I mean if they immunize my kid with something ...  \n",
              "4  Thanks to <user> Catch me performing at La Nui...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61bfb036-b3cd-4210-a670-99943e71f548\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>safe_text</th>\n",
              "      <th>label</th>\n",
              "      <th>agreement</th>\n",
              "      <th>extract_hashtags</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL1KWCMY</td>\n",
              "      <td>Me &amp;amp; The Big Homie meanboy3000 #MEANBOY #M...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MEANBOY MB MBS MMR STEGMANLIFE</td>\n",
              "      <td>Me  amp  The Big Homie meanboy3000  MEANBOY  M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E3303EME</td>\n",
              "      <td>I'm 100% thinking of devoting my career to pro...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td></td>\n",
              "      <td>I m 100% thinking of devoting my career to pro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M4IVFSMS</td>\n",
              "      <td>#whatcausesautism VACCINES, DO NOT VACCINATE Y...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>whatcausesautism</td>\n",
              "      <td>whatcausesautism VACCINES  DO NOT VACCINATE Y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1DR6ROZ4</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td></td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>J77ENIIE</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>jennifair mmr…</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61bfb036-b3cd-4210-a670-99943e71f548')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-61bfb036-b3cd-4210-a670-99943e71f548 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-61bfb036-b3cd-4210-a670-99943e71f548');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b52553c5-1fdf-411c-b02b-d0a91471782f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b52553c5-1fdf-411c-b02b-d0a91471782f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b52553c5-1fdf-411c-b02b-d0a91471782f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We Replace ['-', '\"', 'u'] with [ ]\n",
        "words_to_replace_3 = ['-', '\"', 'u' ]\n",
        "\n",
        "# Pattern to match any of the words in the list, using a regular expression\n",
        "pattern_3  = r'\\b(?:{})\\b'.format('|'.join(words_to_replace_3 ))\n",
        "\n",
        "# Function to replace the words with 'vaccine'\n",
        "def replace_with_vaccine_3 (text):\n",
        "    return text.str.replace(pattern_3 , '', case=False)\n",
        "\n",
        "# Apply the function to the 'safe_text' column\n",
        "df['clean_text'] = replace_with_vaccine_3(df['clean_text'])"
      ],
      "metadata": {
        "id": "u3ehA8R-ugcQ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b5Gae7GQugm-"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i1p1asp9ugqQ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['tweet_id','clean_text','label','agreement']]"
      ],
      "metadata": {
        "id": "DvNWFmzLJVch"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "blaZMM_DjFbi",
        "outputId": "df1f9141-1201-4791-ab3f-12c62935b6d4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   tweet_id                                         clean_text  label  \\\n",
              "0  CL1KWCMY  Me  amp  The Big Homie meanboy3000  MEANBOY  M...    0.0   \n",
              "1  E3303EME  I m 100% thinking of devoting my career to pro...    1.0   \n",
              "2  M4IVFSMS   whatcausesautism VACCINES  DO NOT VACCINATE Y...   -1.0   \n",
              "3  1DR6ROZ4  I mean if they immunize my kid with something ...   -1.0   \n",
              "4  J77ENIIE  Thanks to <user> Catch me performing at La Nui...    0.0   \n",
              "\n",
              "   agreement  \n",
              "0        1.0  \n",
              "1        1.0  \n",
              "2        1.0  \n",
              "3        1.0  \n",
              "4        1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b9bb3c1-18e9-49a7-b1cb-319c03e9e44e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>clean_text</th>\n",
              "      <th>label</th>\n",
              "      <th>agreement</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL1KWCMY</td>\n",
              "      <td>Me  amp  The Big Homie meanboy3000  MEANBOY  M...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E3303EME</td>\n",
              "      <td>I m 100% thinking of devoting my career to pro...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M4IVFSMS</td>\n",
              "      <td>whatcausesautism VACCINES  DO NOT VACCINATE Y...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1DR6ROZ4</td>\n",
              "      <td>I mean if they immunize my kid with something ...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>J77ENIIE</td>\n",
              "      <td>Thanks to &lt;user&gt; Catch me performing at La Nui...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b9bb3c1-18e9-49a7-b1cb-319c03e9e44e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8b9bb3c1-18e9-49a7-b1cb-319c03e9e44e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8b9bb3c1-18e9-49a7-b1cb-319c03e9e44e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-24b026d3-5214-4ab8-9a52-11e38f7736b5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24b026d3-5214-4ab8-9a52-11e38f7736b5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugCzfy2G25ZX",
        "outputId": "be226ee6-b0cb-4179-f7be-acb5ecac5329"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 9999 entries, 0 to 10000\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   tweet_id    9999 non-null   object \n",
            " 1   clean_text  9999 non-null   object \n",
            " 2   label       9999 non-null   float64\n",
            " 3   agreement   9999 non-null   float64\n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 390.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Splitting"
      ],
      "metadata": {
        "id": "l2EvIkfBIqTx"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxVhp3rlBcJK"
      },
      "source": [
        "We will split the training set to have a training subset ( a dataset the model will learn on), and an evaluation subset ( a dataset the model with use to compute metric scores to help use to avoid some training problems like [the overfitting](https://www.ibm.com/cloud/learn/overfitting) one ).\n",
        "\n",
        "There are multiple ways to do split the dataset.You will see two commented line showing you another one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "zGTlFkqGBcJK"
      },
      "outputs": [],
      "source": [
        "# Split the train data => {train, eval}\n",
        "train, eval = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "57GZnWphBcJL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5d6011a7-23e7-4978-ac7c-5b5c22d5f2d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      tweet_id                                         clean_text  label  \\\n",
              "9305  YMRMEDME      Mickey s Measles has gone international <url>    0.0   \n",
              "3907  5GV8NEZS  S1256 [NEW] Extends exemption from charitable ...    0.0   \n",
              "795   EI10PS46  <user>  your ignorance on vaccines isn t just ...    1.0   \n",
              "5793  OM26E6DG  Pakistan partly suspends polio vaccination pro...    0.0   \n",
              "3431  NBBY86FX           In other news I ve gone up like 1000 mmr    0.0   \n",
              "\n",
              "      agreement  \n",
              "9305   1.000000  \n",
              "3907   1.000000  \n",
              "795    0.666667  \n",
              "5793   1.000000  \n",
              "3431   1.000000  "
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              "        const element = document.querySelector('#df-2c91731e-2d3f-4c39-bd50-1407af1ad695');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "train.head()"
      ]
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      "execution_count": 53,
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          "height": 206
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        "outputId": "8a5a7352-508e-4ab6-ee62-33de291376b9"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      tweet_id                                         clean_text  label  \\\n",
              "6571  R7JPIFN7  Children s Museum of Houston to Offer Free Vac...    1.0   \n",
              "1754  2DD250VN  <user> no  I was properly immunized prior to t...    1.0   \n",
              "3325  ESEVBTFN  <user> thx for posting vaccinations are impera...    1.0   \n",
              "1485  S17ZU0LC  This Baby Is Exactly Why Everyone Needs To Vac...    1.0   \n",
              "4175  IIN5D33V  Meeting tonight  8:30pm in room 322 of the stu...    1.0   \n",
              "\n",
              "      agreement  \n",
              "6571   1.000000  \n",
              "1754   1.000000  \n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4bf800e0-109c-4e8d-9150-ed8c46888430 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4bf800e0-109c-4e8d-9150-ed8c46888430');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b622f0bf-1ef1-44d3-9e8d-cb3176b33259\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b622f0bf-1ef1-44d3-9e8d-cb3176b33259')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b622f0bf-1ef1-44d3-9e8d-cb3176b33259 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "eval.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "K_81PZCMBcJQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64990f9b-a6d8-469e-9bdb-d96e6673a27a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new dataframe shapes: train is (7999, 4), eval is (2000, 4)\n"
          ]
        }
      ],
      "source": [
        "print(f\"new dataframe shapes: train is {train.shape}, eval is {eval.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will create a directory and save the 'train_subset.csv' and 'eval_subset.csv' files within that directory."
      ],
      "metadata": {
        "id": "pHJyilkXOJlX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To create a director to hold the datasets\n",
        "if not os.path.exists('LP5 Dataset'):\n",
        "  os.makedirs('LP5 Dataset')"
      ],
      "metadata": {
        "id": "swg2Ti_WOa1U"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To check if the directory has been created\n",
        "if os.path.exists('LP5 Dataset'):\n",
        "  print('The dataset directory exists')\n",
        "else:\n",
        "  print(\"The dataset directory does not exist\")"
      ],
      "metadata": {
        "id": "EwC2XUYiOzKi",
        "outputId": "d72ce4e4-5de0-42ae-9a57-9d3c0315c00d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset directory exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "FNK6iN2dBcJR"
      },
      "outputs": [],
      "source": [
        "# Save splitted subsets in the dataset folder we created\n",
        "train.to_csv(\"./LP5 Dataset/train_subset.csv\", index=False)\n",
        "eval.to_csv(\"./LP5 Dataset/eval_subset.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "hVNAYOvrBcJS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "44764a4ff61b43a49f7c32a091266ebe"
          ]
        },
        "outputId": "182b5cb2-8ed7-4207-e24e-5f0771ac4816"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7074986ff2b342e79f488e9ff1d87729"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "735f7e23bff942a38341ead479e4cbf4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "568a8729819342f4b43fb5181f6dc45c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating eval split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a64cd28d98a94cf79bd98cc30640f7da"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset('csv',\n",
        "                        data_files={'train': './LP5 Dataset/train_subset.csv',\n",
        "                        'eval': './LP5 Dataset/eval_subset.csv'}, encoding = \"ISO-8859-1\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To check if the dataset has been loaded properly\n",
        "dataset"
      ],
      "metadata": {
        "id": "3z183zsNfBS8",
        "outputId": "de0b40c7-8b7c-4e76-812c-b471496e2543",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement'],\n",
              "        num_rows: 7999\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BERT-BASED  MODEL\n",
        "BERT (Bidirectional Encoder Representations from Transformers) is a popular pre-trained model architecture for natural language understanding and processing. The \"base\" version of BERT, often referred to as \"BERT base,\" represents a standard configuration of the BERT model. The \"cased\" variant of BERT retains the original casing of words in the pre-trained embeddings, which means it differentiates between uppercase and lowercase characters.\n",
        "\n",
        "Here's what \"BERT base cased\" typically refers to:\n",
        "\n",
        "1. **Base Model**: The \"base\" version of BERT usually refers to a model with a certain number of layers, hidden units, and attention heads. The specific architecture can vary, but it's a medium-sized version of BERT that balances computational cost and performance. For instance, it might have 12 layers, 768 hidden units, and 12 attention heads.\n",
        "\n",
        "2. **Cased**: The \"cased\" variant of BERT retains the case information of words. This means that it treats \"Word\" and \"word\" as distinct tokens, capturing the difference in case. This can be important for tasks where the case of words carries semantic meaning.\n",
        "\n",
        "BERT base cased is trained on a large corpus of text and can be fine-tuned for various natural language processing tasks, such as text classification, named entity recognition, question-answering, and more. Researchers and practitioners often use BERT base cased as a starting point for their NLP tasks because it provides strong out-of-the-box performance.\n",
        "\n",
        "You can access pre-trained BERT base cased models and their corresponding tokenizers from the Hugging Face Transformers library, which is a popular library for working with pre-trained models in NLP. These models are available in various languages and can be loaded and fine-tuned for specific NLP tasks.\n",
        "\n",
        "If you have more specific questions about using BERT base cased for a particular task or need code examples, feel free to ask!\n",
        "\n",
        "\n",
        "\n",
        "## Preprocessing\n",
        "Tokenization is the process of splitting raw text into smaller units called tokens. Tokens are typically words or subwords. Tokenization is a fundamental step in NLP because it breaks down text data into manageable units that can be processed by NLP models. In this case we are using a pretrained tokenizer from Hugging Face's Transformers library.\n",
        "\n",
        "We will import the AutoTokenizer class from the Hugging Face Transformers library and then create an instance of the AutoTokenizer class and initializes it with a pre-trained BERT tokenizer."
      ],
      "metadata": {
        "id": "iwyvf9pTfShy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "KaqYVJK_BcJW",
        "outputId": "bf92b21f-c7dc-4f6d-beae-584964a8c126",
        "colab": {
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          "height": 145,
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "932aa134ff5042a890a81706c05d0fdd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73f78905d76b456f8f200b6ad7cd5e1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b16eae53090c46a49c544c7749e3bd65"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import Autokenizer\n",
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define a function tokenize_function takes an input argument, which is expected to be a dictionary. This dictionary should contain a key 'text' that corresponds to the text data you want to tokenize. The function, then calls the tokenizer object that was initialized earlier. It tokenizes the text data provided.\n",
        "\n",
        "Padding is the process of adding special tokens (often <PAD>) to the sequences to make them uniform in length. Setting padding='max_length' in the tokenizer ensures that all tokenized sequences have the same maximum length.\n",
        "\n",
        "\n",
        "The padding argument specifies that the tokenized sequences should be padded to the maximum sequence length in the batch. Padding ensures that all sequences have the same length.\n"
      ],
      "metadata": {
        "id": "h4cy94Cip9j6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#A function to tockenize the data\n",
        "def tokenize_function(df):\n",
        "    return tokenizer(df['clean_text'], padding='max_length')\n"
      ],
      "metadata": {
        "id": "e8lSf734p8sv"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We apply the tokenize_data function to the dataset using the map function, in a\n",
        "batched manner. This will tokenize each text field and padding the sequences to the maximum length in the batch.\n",
        "\n",
        "The argument 'batched=True' allows to efficiently tokenize and pad multiple examples in a batch. Batch processing is essential for speeding up NLP tasks, as it allows the processing of multiple examples in parallel, which is especially beneficial when working with large datasets."
      ],
      "metadata": {
        "id": "q601rgbktxrO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Change the tweets to tokens that the models can exploit\n",
        "dataset = dataset.map(tokenize_function, batched=True)\n"
      ],
      "metadata": {
        "id": "Z60Z-UXBtylx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "outputId": "5de7c8ac-5722-495d-d510-e0a2ab1e1c99"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8874bcfc146945d59f0e57408f843a9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define a function that will transform the sentiments labels from -1, 0, and 1 and converts them into a numerical format such that -1 becomes 0, 0 becomes 1, and 1 becomes 3."
      ],
      "metadata": {
        "id": "FX012QXOpIYo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "IQfSWsGeBcJX"
      },
      "outputs": [],
      "source": [
        "def transform_labels(label):\n",
        "\n",
        "    label = label['label']\n",
        "    num = 0\n",
        "    if label == -1: #'Negative'\n",
        "        num = 0\n",
        "    elif label == 0: #'Neutral'\n",
        "        num = 1\n",
        "    elif label == 1: #'Positive'\n",
        "        num = 2\n",
        "\n",
        "    return {'labels': num}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform\tlabels and remove the useless columns\n",
        "remove_columns = ['tweet_id', 'label', 'agreement','clean_text']\n",
        "dataset = dataset.map(transform_labels, remove_columns=remove_columns)"
      ],
      "metadata": {
        "id": "nrenJtU3uWa_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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          ]
        },
        "outputId": "aa9d9eb0-060b-4bc7-b615-ab4c8e48be7c"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6a8e51a8725d4506844a2e43f1fd574d"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "48DfqbciBcJY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e461277d-7827-4362-de1a-a2afaeacb85c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 7999\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "1IecARwHBcJZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8af27a12-4045-4827-976c-b6f9dcc028ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
          ]
        }
      ],
      "source": [
        "# SPecifying the training arguments\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "# Configure the trianing parameters like `num_train_epochs`:\n",
        "# the number of time the model will repeat the training loop over the dataset\n",
        "training_args = TrainingArguments(\"Covid_Vaccine_Sentiment_Analysis_Bert_based_Model\",\n",
        "                                  num_train_epochs=10,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  push_to_hub=True,\n",
        "                                  evaluation_strategy=\"steps\",\n",
        "                                  save_strategy=\"steps\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The AutoModelForSequenceClassification provides a unified interface for loading various pre-trained models (like BERT, RoBERTa, etc.) and fine-tuning them for sequence classification tasks.WE will load the pre-trained BERT model and configures it for sequence classification with the specified number of labels"
      ],
      "metadata": {
        "id": "iShTPHN3oPAC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "6u0di0yoBcJZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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        },
        "outputId": "420f1c8e-0236-46e6-f0d8-5769164c4660"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "810f2fc0fef84862bceab00a7b17545c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# We import the AutomodelForSequenceClassificatio class from the Transformers library\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "# Loading a pretrain model while specifying the number of labels in our dataset for fine-tuning\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "_N7-R2QRBcJa"
      },
      "outputs": [],
      "source": [
        "# We shuffle the dataset to randomnize the data and avoid any bias\n",
        "train_dataset = dataset['train'].shuffle(seed=10)\n",
        "eval_dataset = dataset['eval'].shuffle(seed=10)\n",
        "\n",
        "## other way to split the train set ... in the range you must use:\n",
        "# # int(num_rows*.8 ) for [0 - 80%] and  int(num_rows*.8 ),num_rows for the 20% ([80 - 100%])\n",
        "# train_dataset = dataset['train'].shuffle(seed=10).select(range(40000))\n",
        "# eval_dataset = dataset['train'].shuffle(seed=10).select(range(40000, 41000))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We initialize the Trainer object from the Transformers library. The Trainer class is a high-level API that simplifies the training and evaluation of transformer-based models for various"
      ],
      "metadata": {
        "id": "jHRyI-1XpmQd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "_EeeI3nGBcJb"
      },
      "outputs": [],
      "source": [
        "# WE import the Trainer class from the Transformers library.\n",
        "from transformers import Trainer\n",
        "\n",
        "# We initialize the trainer object and specify the arguments\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "Oj9W6nU4BcJb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "fbdb9804-3ad3-4026-b0e3-e56ed5a60d33"
      },
      "outputs": [
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              "<IPython.core.display.HTML object>"
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              "    <div>\n",
              "      \n",
              "      <progress value='10000' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10000/10000 2:22:19, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
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              "      <td>500</td>\n",
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              "      <td>0.071800</td>\n",
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              "      <td>9500</td>\n",
              "      <td>0.019700</td>\n",
              "      <td>2.118986</td>\n",
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              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.014700</td>\n",
              "      <td>2.106975</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10000, training_loss=0.24322458848953246, metrics={'train_runtime': 8540.4084, 'train_samples_per_second': 9.366, 'train_steps_per_second': 1.171, 'total_flos': 2.104644228406272e+16, 'train_loss': 0.24322458848953246, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "# Launch the learning process: training\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To push the trained model into hugging face model hub\n",
        "trainer.push_to_hub()\n"
      ],
      "metadata": {
        "id": "BcrMys9ejMGp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "1ed4e019-32d7-438a-c786-0f1a85bdc918"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/NewtonKimathi/Covid_Vaccine_Sentiment_Analysis_Bert_based_Model/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(\"NewtonKimathi/Covid_Vaccine_Sentiment_Analysis_Bert_based_Model\")"
      ],
      "metadata": {
        "id": "mqrhPcEk94Ye",
        "outputId": "e0b3e662-bb9a-4cc6-cb45-958c0ff96b92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/NewtonKimathi/Covid_Vaccine_Sentiment_Analysis_Bert_based_Model/commit/4e7f1e6b1a4f0714c04446d2bc10467724e57711', commit_message='Upload tokenizer', commit_description='', oid='4e7f1e6b1a4f0714c04446d2bc10467724e57711', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGUIpe2sBcJc"
      },
      "source": [
        "Don't worry the above issue, it is a `KeyboardInterrupt` that means I stopped the training to avoid taking a long time to finish."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "CWqTjv-TBcJd"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "1Wr8MMcLBcJd"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "O329L-KzBcJe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "511fc98c-0d23-4da6-8ff7-79b269be87ee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 01:02]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.6179400682449341,\n",
              " 'eval_accuracy': 0.744,\n",
              " 'eval_runtime': 63.1494,\n",
              " 'eval_samples_per_second': 31.671,\n",
              " 'eval_steps_per_second': 3.959}"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "# Launch the final evaluation\n",
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WTFTUyKBcJf"
      },
      "source": [
        "# Model 2 : Roberta\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaXyqAwMBcJf"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNAvKF5mBcJf"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0aC9UonYBcJg"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import RobertaForSequenceClassification, RobertaTokenizer\n",
        "\n",
        "# Load pre-trained RoBERTa model and tokenizer\n",
        "tokenizer_2 = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
        "model2 = RobertaForSequenceClassification.from_pretrained(\"roberta-base\", num_labels=3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
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            "f0a27ec1bf2a45e5b266a4e3672b551b"
          ]
        },
        "id": "aj7xFArtUJqc",
        "outputId": "f24ca48c-9132-4a05-b450-935db97635bb"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "766de4aff3eb46d1afa1ec2cb5878093"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8f0e68d49f44820a080d293f83883ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "caddb7cef2cd4a9dab1c987869a61845"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3d9696377f4437287eca013b5c3ead0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# have the tokenize function with the Tokenizer 2\n",
        "\n",
        "def tokenize_function_2(df):\n",
        "    return tokenizer_2(df['clean_text'], padding=\"max_length\")"
      ],
      "metadata": {
        "id": "q_zFwXv3Unus"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using the load_dataset function to load CSV files as datasets\n",
        "dataset_2 = load_dataset('csv',\n",
        "                        data_files={'train': './LP5 Dataset/train_subset.csv',\n",
        "                        'eval': './LP5 Dataset/eval_subset.csv'}, encoding = \"ISO-8859-1\")"
      ],
      "metadata": {
        "id": "n2ZXCiN0UwCC"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iNhA2HEVpjZ",
        "outputId": "2a76404b-46ec-446a-9cbf-36eeed162717"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement'],\n",
              "        num_rows: 7999\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the dataset\n",
        "# Changing the tweets into tokens our model can explot\n",
        "\n",
        "dataset_2 = dataset_2.map(tokenize_function_2, batched=True)"
      ],
      "metadata": {
        "id": "aWwrFrYSVtvq",
        "outputId": "e74e99e4-7601-4ba7-c342-e9ee3a77d557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        }
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4afa2fc7ba0c4e769bf7889b06c4894d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnIx090Meyea",
        "outputId": "90066f2d-d3c9-42d2-e142-aff0871e51ca"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 7999\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['tweet_id', 'clean_text', 'label', 'agreement', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_labels(data):\n",
        "    label = data['label']  # extracts the value of the 'label' from the data input\n",
        "    num = 0\n",
        "    if label == -1:  # 'Negative' sentiment\n",
        "        num = 0\n",
        "    elif label == 0:  # 'Neutral' sentiment\n",
        "        num = 1\n",
        "    elif label == 1:  # 'Positive' sentiment\n",
        "        num = 2\n",
        "    return {\"labels\": num}"
      ],
      "metadata": {
        "id": "shpbwb29XZN6"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you are using the 'transform_labels' function for the mapping\n",
        "drop = ['tweet_id', 'clean_text', 'label', 'agreement']\n",
        "dataset_2 = dataset_2.map(transform_labels, remove_columns=drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        "id": "dXUCCzUXXhCH",
        "outputId": "042b7156-b42b-49b0-9d4b-76a2dd294cc2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7999 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b158451ad0e243b5a614799f16887cf2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7ff8991ab554c68a40759313e77bd71"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWtF-dmleTzz",
        "outputId": "49cff4e6-d007-4b92-bc30-7cee5bf5da51"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 7999\n",
              "    })\n",
              "    eval: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Shuffle the dataset\n",
        "\n",
        "roberta_train_dataset = dataset_2[\"train\"].shuffle(seed=50)#.take(subset_size)\n",
        "roberta_eval_dataset = dataset_2[\"eval\"].shuffle(seed=50)"
      ],
      "metadata": {
        "id": "Y6Husc4_XrDd"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roberta_eval_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t59mqqWGYVWR",
        "outputId": "16bb91f6-0ed8-4842-de63-9aff2c55ae6c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask', 'labels'],\n",
              "    num_rows: 2000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SPecifying the training arguments\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "# Configure the trianing parameters like `num_train_epochs`:\n",
        "# the number of time the model will repeat the training loop over the dataset\n",
        "training_args_2 = TrainingArguments(\"Covid_Vaccine_Sentiment_Analysis_Roberta_Model\",\n",
        "                                  num_train_epochs=5,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  push_to_hub=True,\n",
        "                                  evaluation_strategy=\"steps\",\n",
        "                                  save_strategy=\"steps\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaRYQqMUYjh_",
        "outputId": "9bfa5ca1-dcc7-4657-8553-be263363b63a"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# WE import the Trainer class from the Transformers library.\n",
        "from transformers import Trainer\n",
        "# Create a trainer\n",
        "\n",
        "trainer_2 = Trainer(model = model2,args = training_args_2,train_dataset = roberta_train_dataset,\n",
        "                    eval_dataset = roberta_eval_dataset)"
      ],
      "metadata": {
        "id": "S3YpKJ7GYl9h"
      },
      "execution_count": 78,
      "outputs": []
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      "cell_type": "code",
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        "trainer_2.train()"
      ],
      "metadata": {
        "colab": {
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          "height": 441
        },
        "id": "t_Fdt9upaWdX",
        "outputId": "3af23c77-0f1e-4218-dce3-12fb7a0738cb"
      },
      "execution_count": 79,
      "outputs": [
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5000' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5000/5000 1:10:34, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.793300</td>\n",
              "      <td>0.714130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.786900</td>\n",
              "      <td>0.762703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.732500</td>\n",
              "      <td>0.691696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.729200</td>\n",
              "      <td>0.686629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.686100</td>\n",
              "      <td>0.717395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.680000</td>\n",
              "      <td>0.693970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.622000</td>\n",
              "      <td>0.686874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.627800</td>\n",
              "      <td>0.700868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.563100</td>\n",
              "      <td>0.732568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.558900</td>\n",
              "      <td>0.702088</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5000, training_loss=0.6779822937011719, metrics={'train_runtime': 4238.9541, 'train_samples_per_second': 9.435, 'train_steps_per_second': 1.18, 'total_flos': 1.052322114203136e+16, 'train_loss': 0.6779822937011719, 'epoch': 5.0})"
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          },
          "metadata": {},
          "execution_count": 79
        }
      ]
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        "outputId": "a1570b1e-c589-4b49-dad2-5f75004fe0cc"
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      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "\n",
        "def compute_Metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
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        "trainer_2 = Trainer(\n",
        "    model=model2,\n",
        "    args=training_args_2,\n",
        "    train_dataset=roberta_train_dataset,\n",
        "    eval_dataset=roberta_eval_dataset,\n",
        "    compute_metrics=compute_Metrics,\n",
        ")"
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      "outputs": []
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              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 00:57]\n",
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              " 'eval_accuracy': 0.7365,\n",
              " 'eval_runtime': 57.6464,\n",
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