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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4qLQv5HhgfhYwAxUkXTZb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JoanWaweru/Sentiment-Analysis-for-Code-Switched-Language/blob/main/SafaricomProject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reading Dataset "
      ],
      "metadata": {
        "id": "AqlyY3SkUqDA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HZk0cjYKyTU",
        "outputId": "2b207f93-8bd5-4e3f-83a0-a0258db637a4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.4)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "FF7lkTvWK3_m"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read csv file into a pandas dataframe\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "Z-KmY2IUK-ZN",
        "outputId": "ac45d808-bd13-4b81-8ad7-348ddd31eea5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2afccbb4-2da5-445a-8f43-4a7981932ab4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2afccbb4-2da5-445a-8f43-4a7981932ab4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving safaricomDataset.csv to safaricomDataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('safaricomDataset.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "3vmKstfoWCqE",
        "outputId": "61984899-73da-4ab6-9b7f-73ee76e3a1a0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                       Date            User  \\\n",
              "0           0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1           1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2           2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3           3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4           4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...  \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...  \n",
              "2                           @Safaricom_Care check DM  \n",
              "3  @martinkanini Hello Martin, please DM https://...  \n",
              "4  @Safaricom_Care am getting some OTP several of...  "
            ],
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              "      <th>Unnamed: 0</th>\n",
              "      <th>Date</th>\n",
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              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
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              "      <td>2</td>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
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              "      <td>@Safaricom_Care check DM</td>\n",
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              "      <td>3</td>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
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              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
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        "df.columns"
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      "metadata": {
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        "id": "MFT9IG46XnmQ",
        "outputId": "06a0b1c6-1791-4974-ed09-d2d7523aa324"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Date', 'User', 'Tweet'], dtype='object')"
            ]
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          "metadata": {},
          "execution_count": 5
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.shape"
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        "outputId": "7e65eb61-f3bf-47f2-f546-f74f5487d161"
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      "outputs": [
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            "text/plain": [
              "(5000, 4)"
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "tweets_df = df[[\"Date\", \"User\", \"Tweet\"]]\n",
        "tweets_df.head()"
      ],
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            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...  \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...  \n",
              "2                           @Safaricom_Care check DM  \n",
              "3  @martinkanini Hello Martin, please DM https://...  \n",
              "4  @Safaricom_Care am getting some OTP several of...  "
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>User</th>\n",
              "      <th>Tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-10-23 23:37:49+00:00</td>\n",
              "      <td>samsabwa</td>\n",
              "      <td>@waxspell @Safaricom_Care @AskPayPal sponyo al...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-10-23 23:36:25+00:00</td>\n",
              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import utils\n",
        "tweets_df.shape"
      ],
      "metadata": {
        "id": "FIEg-0RpYJlt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "980c00f7-21f3-4e7d-d8f4-a182f8f8fb35"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocessing and Cleaning of the Dataset "
      ],
      "metadata": {
        "id": "1NFIf7e3Y9mK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "metadata": {
        "id": "31oz3sSaY9HA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHnxrYOSaLu7",
        "outputId": "b05ca92d-6411-462b-b477-9bccc957b4bf"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install emoji"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhJsCN80aNZO",
        "outputId": "80c2062b-a23a-422e-f5b8-ad6fe06c9ebd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting emoji\n",
            "  Downloading emoji-2.1.0.tar.gz (216 kB)\n",
            "\u001b[K     |████████████████████████████████| 216 kB 7.9 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-2.1.0-py3-none-any.whl size=212392 sha256=bdcacbde4cd6ed0efd4c24b7190969fa8510cb452c80e3584e45f84ee3b95c97\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/75/99/51c2a119f4cfd3af7b49cc57e4f737bed7e40b348a85d82804\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-2.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import emoji\n",
        "def tokenize_tweets(text):\n",
        "  #remove emojis\n",
        "  text = emoji.demojize(text)\n",
        "  #remove urls\n",
        "  text = re.sub('http[s]?://\\S+', '', text)\n",
        "  #remove punctuations\n",
        "  text = re.sub(r'[^\\w\\s]','',text)\n",
        "  #strip numbers\n",
        "  text = re.sub('[0-9]+', '', text)\n",
        "  text = word_tokenize(text)\n",
        "  \n",
        "  return text\n",
        "tweets_df[\"Tweets\"] = tweets_df[\"Tweet\"].apply(lambda x: tokenize_tweets(x))\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "stop = stopwords.words(\"english\")\n",
        "tweets_df[\"stop_words\"] = tweets_df[\"Tweets\"].apply(lambda x: [w for w in x if w in stop])\n",
        "tweets_df[\"Tweets\"] = tweets_df[\"Tweets\"].apply(lambda x: [w.lower() for w in x if w not in stop])\n",
        "\n",
        "tweets_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "XxKF0TlbaSXd",
        "outputId": "f0746fae-c5dd-45bc-c8ab-c0da5474cdc2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "5  2022-10-23 21:48:31+00:00      Reyherb254   \n",
              "6  2022-10-23 21:29:20+00:00     Daktari_Kip   \n",
              "7  2022-10-23 21:20:20+00:00  Safaricom_Care   \n",
              "8  2022-10-23 21:11:45+00:00        RotinhoD   \n",
              "9  2022-10-23 21:08:48+00:00         c_hir_i   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "5  @Cutie_254 @Safaricom_Care @iconic_gravity @it...   \n",
              "6                                 Fb @Safaricom_Care   \n",
              "7  @c_hir_i Hello, you can view detailed data usa...   \n",
              "8  @Safaricom_Care  message kaa hii ni kitu mnaan...   \n",
              "9  #saf_for_you\\n🫤🙄I guess niko #6G \\n@Safaricom_...   \n",
              "\n",
              "                                              Tweets  \\\n",
              "0  [waxspell, safaricom_care, askpaypal, sponyo, ...   \n",
              "1  [safaricom_care, safaricomplc, official, roami...   \n",
              "2                        [safaricom_care, check, dm]   \n",
              "3  [martinkanini, hello, martin, please, dm, scre...   \n",
              "4  [safaricom_care, getting, otp, several, time, ...   \n",
              "5  [cutie_, safaricom_care, iconic_gravity, itsmj...   \n",
              "6                               [fb, safaricom_care]   \n",
              "7  [c_hir_i, hello, view, detailed, data, usage, ...   \n",
              "8  [safaricom_care, message, kaa, hii, ni, kitu, ...   \n",
              "9  [saf_for_you, face_with_diagonal_mouthface_wit...   \n",
              "\n",
              "                                        stop_words  \n",
              "0                                               []  \n",
              "1                              [what, are, of, is]  \n",
              "2                                               []  \n",
              "3                       [a, of, the, for, to, and]  \n",
              "4      [am, some, of, them, at, this, of, the, my]  \n",
              "5                                               []  \n",
              "6                                               []  \n",
              "7  [you, can, on, this, if, you, a, with, this, a]  \n",
              "8                                               []  \n",
              "9                                               []  "
            ],
            "text/html": [
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              "      <th>Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022-10-23 23:37:49+00:00</td>\n",
              "      <td>samsabwa</td>\n",
              "      <td>@waxspell @Safaricom_Care @AskPayPal sponyo al...</td>\n",
              "      <td>[waxspell, safaricom_care, askpaypal, sponyo, ...</td>\n",
              "      <td>[]</td>\n",
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              "      <th>1</th>\n",
              "      <td>2022-10-23 23:36:25+00:00</td>\n",
              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
              "      <td>[safaricom_care, safaricomplc, official, roami...</td>\n",
              "      <td>[what, are, of, is]</td>\n",
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              "      <th>2</th>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
              "      <td>[safaricom_care, check, dm]</td>\n",
              "      <td>[]</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
              "      <td>[martinkanini, hello, martin, please, dm, scre...</td>\n",
              "      <td>[a, of, the, for, to, and]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
              "      <td>[safaricom_care, getting, otp, several, time, ...</td>\n",
              "      <td>[am, some, of, them, at, this, of, the, my]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-10-23 21:48:31+00:00</td>\n",
              "      <td>Reyherb254</td>\n",
              "      <td>@Cutie_254 @Safaricom_Care @iconic_gravity @it...</td>\n",
              "      <td>[cutie_, safaricom_care, iconic_gravity, itsmj...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-10-23 21:29:20+00:00</td>\n",
              "      <td>Daktari_Kip</td>\n",
              "      <td>Fb @Safaricom_Care</td>\n",
              "      <td>[fb, safaricom_care]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-10-23 21:20:20+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@c_hir_i Hello, you can view detailed data usa...</td>\n",
              "      <td>[c_hir_i, hello, view, detailed, data, usage, ...</td>\n",
              "      <td>[you, can, on, this, if, you, a, with, this, a]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-10-23 21:11:45+00:00</td>\n",
              "      <td>RotinhoD</td>\n",
              "      <td>@Safaricom_Care  message kaa hii ni kitu mnaan...</td>\n",
              "      <td>[safaricom_care, message, kaa, hii, ni, kitu, ...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-10-23 21:08:48+00:00</td>\n",
              "      <td>c_hir_i</td>\n",
              "      <td>#saf_for_you\\n🫤🙄I guess niko #6G \\n@Safaricom_...</td>\n",
              "      <td>[saf_for_you, face_with_diagonal_mouthface_wit...</td>\n",
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              "                                                     [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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df.head()"
      ],
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        "colab": {
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "\n",
              "                                              Tweets  \\\n",
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              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
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              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
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              "      <td>martinkanini</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "string.punctuation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5IJf_zIialV_",
        "outputId": "ab0f14d8-3012-4c60-84b0-c61e392af345"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.porter import *\n",
        "stemmer = PorterStemmer()\n",
        "tweets_df[\"Tweets\"] = tweets_df[\"Tweets\"].apply(lambda x: [stemmer.stem(w) for w in x])\n",
        "tweets_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "e22nq9ixatcv",
        "outputId": "6c91f28f-71cd-4166-d45a-56219b944e86"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "\n",
              "                                              Tweets  \\\n",
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              "\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_punct(text):\n",
        "  text = \" \".join([char for char in text if char not in string.punctuation])\n",
        "  text = re.sub('[0-9]+', '', text)\n",
        "  \n",
        "  \n",
        "  return text\n",
        "tweets_df['tweet_punct'] = tweets_df['Tweets'].apply(lambda x: remove_punct(x))"
      ],
      "metadata": {
        "id": "lgCOW-6va39P"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "WQwGF21Ua8Bh",
        "outputId": "1ac105da-1c5e-4bff-81d9-dace034f1376"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "\n",
              "                                              Tweets  \\\n",
              "0  [waxspel, safaricom_car, askpayp, sponyo, alif...   \n",
              "1  [safaricom_car, safaricomplc, offici, roam, ra...   \n",
              "2                         [safaricom_car, check, dm]   \n",
              "3  [martinkanini, hello, martin, pleas, dm, scree...   \n",
              "4  [safaricom_car, get, otp, sever, time, night, ...   \n",
              "\n",
              "                                    stop_words  \\\n",
              "0                                           []   \n",
              "1                          [what, are, of, is]   \n",
              "2                                           []   \n",
              "3                   [a, of, the, for, to, and]   \n",
              "4  [am, some, of, them, at, this, of, the, my]   \n",
              "\n",
              "                                         tweet_punct  \n",
              "0  waxspel safaricom_car askpayp sponyo alifinya ...  \n",
              "1  safaricom_car safaricomplc offici roam rate ma...  \n",
              "2                             safaricom_car check dm  \n",
              "3  martinkanini hello martin pleas dm screenshot ...  \n",
              "4  safaricom_car get otp sever time night is numb...  "
            ],
            "text/html": [
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>User</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>stop_words</th>\n",
              "      <th>tweet_punct</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022-10-23 23:37:49+00:00</td>\n",
              "      <td>samsabwa</td>\n",
              "      <td>@waxspell @Safaricom_Care @AskPayPal sponyo al...</td>\n",
              "      <td>[waxspel, safaricom_car, askpayp, sponyo, alif...</td>\n",
              "      <td>[]</td>\n",
              "      <td>waxspel safaricom_car askpayp sponyo alifinya ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-10-23 23:36:25+00:00</td>\n",
              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
              "      <td>[safaricom_car, safaricomplc, offici, roam, ra...</td>\n",
              "      <td>[what, are, of, is]</td>\n",
              "      <td>safaricom_car safaricomplc offici roam rate ma...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
              "      <td>[safaricom_car, check, dm]</td>\n",
              "      <td>[]</td>\n",
              "      <td>safaricom_car check dm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
              "      <td>[martinkanini, hello, martin, pleas, dm, scree...</td>\n",
              "      <td>[a, of, the, for, to, and]</td>\n",
              "      <td>martinkanini hello martin pleas dm screenshot ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
              "      <td>[safaricom_car, get, otp, sever, time, night, ...</td>\n",
              "      <td>[am, some, of, them, at, this, of, the, my]</td>\n",
              "      <td>safaricom_car get otp sever time night is numb...</td>\n",
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              "          const element = document.querySelector('#df-8d59aa6a-d2bb-4d08-abd3-0c8111b1a48e');\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization(Word Cloud)"
      ],
      "metadata": {
        "id": "ZUjEFuP6dQyN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "all_words = ' '.join([text for text in df['Tweet']])\n",
        "from wordcloud import WordCloud\n",
        "wordcloud = WordCloud(width=800, height=500, random_state=21, max_font_size=110).generate(all_words)\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "XG12w2zvdU25",
        "outputId": "992e3d82-1c63-4d08-f443-bc26ba0e1d96"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Get the most frequent words"
      ],
      "metadata": {
        "id": "QhmkMuE0eg4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "\n",
        "cnt = Counter()\n",
        "for text in df[\"Tweet\"].values:\n",
        "    for word in text.split():\n",
        "        cnt[word] += 1\n",
        "        \n",
        "cnt.most_common(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALEvU2M2ejBn",
        "outputId": "41290f80-e99e-47fc-d428-0ce53b9ba994"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('@Safaricom_Care', 4498),\n",
              " ('the', 1819),\n",
              " ('#Tuinuane', 1815),\n",
              " ('#CustomerObsessionMonth', 1663),\n",
              " ('to', 1346),\n",
              " ('na', 1314),\n",
              " ('*126#', 1285),\n",
              " ('and', 1246),\n",
              " ('bonga', 1006),\n",
              " ('number', 909),\n",
              " ('or', 875),\n",
              " ('points', 834),\n",
              " ('till', 787),\n",
              " ('Dial', 631),\n",
              " ('you', 592),\n",
              " ('Lipa', 592),\n",
              " ('I', 591),\n",
              " ('for', 577),\n",
              " ('Enter', 556),\n",
              " ('paybill', 549)]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using Vader Library to analyse sentiments in Text"
      ],
      "metadata": {
        "id": "7EwVGCAWeunn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vaderSentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Idg_0LkoexyD",
        "outputId": "e2f477f2-e495-41ab-e6d4-7735ecfbdb1a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[K     |████████████████████████████████| 125 kB 9.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training of Dataset"
      ],
      "metadata": {
        "id": "1x17eVrKZalm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "LZBaJGtLZW9m"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Getting the sentiments label"
      ],
      "metadata": {
        "id": "BQSvmjl2fBr_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sentiment_score_compound(sentence):\n",
        "    score = analyzer.polarity_scores(sentence)\n",
        "    return score['compound']\n",
        "\n",
        "def sentiment_score_pos(sentence):\n",
        "    score = analyzer.polarity_scores(sentence)\n",
        "    return score['pos']\n",
        "\n",
        "def sentiment_score_neg(sentence):\n",
        "    score = analyzer.polarity_scores(sentence)\n",
        "    return score['neg']\n",
        "\n",
        "def sentiment_score_neu(sentence):\n",
        "    score = analyzer.polarity_scores(sentence)\n",
        "    return score['neu']\n",
        "tweets_df[\"tweets_sent_compound\"] = tweets_df[\"Tweet\"].apply(lambda x: sentiment_score_compound(x))\n",
        "tweets_df[\"tweets_sent_pos\"] = tweets_df[\"Tweet\"].apply(lambda x: sentiment_score_pos(x))\n",
        "tweets_df[\"tweets_sent_neg\"] = tweets_df[\"Tweet\"].apply(lambda x: sentiment_score_neg(x))\n",
        "tweets_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "vp3bGJo1fD3c",
        "outputId": "8eab424c-642b-42e0-fce7-bf72b41ff588"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "\n",
              "                                              Tweets  \\\n",
              "0  [waxspel, safaricom_car, askpayp, sponyo, alif...   \n",
              "1  [safaricom_car, safaricomplc, offici, roam, ra...   \n",
              "2                         [safaricom_car, check, dm]   \n",
              "3  [martinkanini, hello, martin, pleas, dm, scree...   \n",
              "4  [safaricom_car, get, otp, sever, time, night, ...   \n",
              "\n",
              "                                    stop_words  \\\n",
              "0                                           []   \n",
              "1                          [what, are, of, is]   \n",
              "2                                           []   \n",
              "3                   [a, of, the, for, to, and]   \n",
              "4  [am, some, of, them, at, this, of, the, my]   \n",
              "\n",
              "                                         tweet_punct  tweets_sent_compound  \\\n",
              "0  waxspel safaricom_car askpayp sponyo alifinya ...                0.0000   \n",
              "1  safaricom_car safaricomplc offici roam rate ma...                0.0000   \n",
              "2                             safaricom_car check dm                0.0000   \n",
              "3  martinkanini hello martin pleas dm screenshot ...                0.3182   \n",
              "4  safaricom_car get otp sever time night is numb...                0.4939   \n",
              "\n",
              "   tweets_sent_pos  tweets_sent_neg  label  \n",
              "0            0.000              0.0      1  \n",
              "1            0.000              0.0      1  \n",
              "2            0.000              0.0      1  \n",
              "3            0.119              0.0      2  \n",
              "4            0.208              0.0      2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a9617e0-b741-4d43-b448-fd1d7dc7f21c\">\n",
              "    <div 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>Date</th>\n",
              "      <th>User</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>stop_words</th>\n",
              "      <th>tweet_punct</th>\n",
              "      <th>tweets_sent_compound</th>\n",
              "      <th>tweets_sent_pos</th>\n",
              "      <th>tweets_sent_neg</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-10-23 23:37:49+00:00</td>\n",
              "      <td>samsabwa</td>\n",
              "      <td>@waxspell @Safaricom_Care @AskPayPal sponyo al...</td>\n",
              "      <td>[waxspel, safaricom_car, askpayp, sponyo, alif...</td>\n",
              "      <td>[]</td>\n",
              "      <td>waxspel safaricom_car askpayp sponyo alifinya ...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-10-23 23:36:25+00:00</td>\n",
              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
              "      <td>[safaricom_car, safaricomplc, offici, roam, ra...</td>\n",
              "      <td>[what, are, of, is]</td>\n",
              "      <td>safaricom_car safaricomplc offici roam rate ma...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
              "      <td>[safaricom_car, check, dm]</td>\n",
              "      <td>[]</td>\n",
              "      <td>safaricom_car check dm</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
              "      <td>[martinkanini, hello, martin, pleas, dm, scree...</td>\n",
              "      <td>[a, of, the, for, to, and]</td>\n",
              "      <td>martinkanini hello martin pleas dm screenshot ...</td>\n",
              "      <td>0.3182</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
              "      <td>[safaricom_car, get, otp, sever, time, night, ...</td>\n",
              "      <td>[am, some, of, them, at, this, of, the, my]</td>\n",
              "      <td>safaricom_car get otp sever time night is numb...</td>\n",
              "      <td>0.4939</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "A2F9DcD3fn3I",
        "outputId": "4fc12a84-f14e-4c48-b57c-cac6b7e7a0c8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Date             User  \\\n",
              "4995  2022-10-20 11:51:05+00:00   campas_kijanaa   \n",
              "4996  2022-10-20 11:51:01+00:00  KiplagatEvans16   \n",
              "4997  2022-10-20 11:50:48+00:00   Safaricom_Care   \n",
              "4998  2022-10-20 11:50:45+00:00    Agropons_Farm   \n",
              "4999  2022-10-20 11:50:45+00:00        Baas_mkuu   \n",
              "\n",
              "                                                  Tweet  \\\n",
              "4995  @Safaricom_Care They were so generous\\n#Tuinua...   \n",
              "4996  @Safaricom_Care Nyi maumbwa hamtanijenga hiyo ...   \n",
              "4997  @muriukiabraham Hey Abraham,  request your mum...   \n",
              "4998  @Safaricom_Care It was great and I was satisfi...   \n",
              "4999  @Safaricom_Care Yes, nilisaidika instantly bil...   \n",
              "\n",
              "                                                 Tweets  \\\n",
              "4995  [safaricom_car, they, gener, tuinuan, customer...   \n",
              "4996  [safaricom_car, nyi, maumbwa, hamtanijenga, hi...   \n",
              "4997  [muriukiabraham, hey, abraham, request, mum, u...   \n",
              "4998  [safaricom_car, it, great, i, satisfi, tuinuan...   \n",
              "4999  [safaricom_car, ye, nilisaidika, instantli, bi...   \n",
              "\n",
              "                                             stop_words  \\\n",
              "4995                                         [were, so]   \n",
              "4996                                                 []   \n",
              "4997  [your, to, the, then, to, the, the, of, the, b...   \n",
              "4998                                    [was, and, was]   \n",
              "4999                          [but, the, is, but, very]   \n",
              "\n",
              "                                            tweet_punct  tweets_sent_compound  \\\n",
              "4995  safaricom_car they gener tuinuan customerobses...                0.6418   \n",
              "4996  safaricom_car nyi maumbwa hamtanijenga hiyo kr...               -0.2960   \n",
              "4997  muriukiabraham hey abraham request mum unblock...                0.0000   \n",
              "4998  safaricom_car it great i satisfi tuinuan custo...                0.7845   \n",
              "4999  safaricom_car ye nilisaidika instantli bilater...                0.6419   \n",
              "\n",
              "      tweets_sent_pos  tweets_sent_neg  \n",
              "4995            0.414            0.000  \n",
              "4996            0.000            0.180  \n",
              "4997            0.000            0.000  \n",
              "4998            0.434            0.000  \n",
              "4999            0.291            0.161  "
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              "      <td>@Safaricom_Care They were so generous\\n#Tuinua...</td>\n",
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              "      <td>KiplagatEvans16</td>\n",
              "      <td>@Safaricom_Care Nyi maumbwa hamtanijenga hiyo ...</td>\n",
              "      <td>[safaricom_car, nyi, maumbwa, hamtanijenga, hi...</td>\n",
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              "      <td>2022-10-20 11:50:48+00:00</td>\n",
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              "      <td>[muriukiabraham, hey, abraham, request, mum, u...</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>2022-10-20 11:50:45+00:00</td>\n",
              "      <td>Agropons_Farm</td>\n",
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              "      <td>[safaricom_car, it, great, i, satisfi, tuinuan...</td>\n",
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              "      <td>safaricom_car it great i satisfi tuinuan custo...</td>\n",
              "      <td>0.7845</td>\n",
              "      <td>0.434</td>\n",
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              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>2022-10-20 11:50:45+00:00</td>\n",
              "      <td>Baas_mkuu</td>\n",
              "      <td>@Safaricom_Care Yes, nilisaidika instantly bil...</td>\n",
              "      <td>[safaricom_car, ye, nilisaidika, instantli, bi...</td>\n",
              "      <td>[but, the, is, but, very]</td>\n",
              "      <td>safaricom_car ye nilisaidika instantli bilater...</td>\n",
              "      <td>0.6419</td>\n",
              "      <td>0.291</td>\n",
              "      <td>0.161</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-93516171-1991-4c0e-8c27-08cad0463fc9');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "wordlist = nltk.FreqDist(all_words)\n",
        "word_features = wordlist.keys()"
      ],
      "metadata": {
        "id": "J1xqCt8qfqxW"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Vectorization"
      ],
      "metadata": {
        "id": "ND0vhG2SgAp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "tweets_list = []\n",
        "for tweet in tweets_df[\"tweet_punct\"]:\n",
        "  \n",
        "  tweets_list.append(tweet)\n",
        "len(tweets_list)\n",
        "tfIdf = TfidfVectorizer(max_features=20000)"
      ],
      "metadata": {
        "id": "kel-Ig9PZz1t"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = tweets_df[\"tweet_punct\"]\n",
        "\n",
        "vec = TfidfVectorizer(min_df=5, max_df=0.95, sublinear_tf = True,use_idf = True,ngram_range=(1, 2))\n",
        "len(all_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyUD75enYGo6",
        "outputId": "a3278947-5a74-4297-9c79-90a828ca6b44"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "546906"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Define Labels(Positive, Negative, Neutral)"
      ],
      "metadata": {
        "id": "i8twbIgIYWIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#negative label is 0 \n",
        "#neutral label is 1 \n",
        "#positive label is 2\n",
        "\n",
        "def label_value(val):\n",
        "  if val < 0:\n",
        "    return 0\n",
        "  elif val == 0:\n",
        "    return 1\n",
        "  else:\n",
        "    return 2\n",
        "tweets_df[\"label\"] = tweets_df[\"tweets_sent_compound\"].apply(lambda x: label_value(x))\n",
        "tweets_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "DFRLQwsnYXiU",
        "outputId": "73d13d00-4ea9-4043-df0a-081b4e783483"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Date            User  \\\n",
              "0  2022-10-23 23:37:49+00:00        samsabwa   \n",
              "1  2022-10-23 23:36:25+00:00          zac124   \n",
              "2  2022-10-23 23:19:35+00:00        tonui_ke   \n",
              "3  2022-10-23 22:14:19+00:00  Safaricom_Care   \n",
              "4  2022-10-23 22:01:01+00:00    martinkanini   \n",
              "\n",
              "                                               Tweet  \\\n",
              "0  @waxspell @Safaricom_Care @AskPayPal sponyo al...   \n",
              "1  @Safaricom_Care @SafaricomPLC \\n\\nwhat are off...   \n",
              "2                           @Safaricom_Care check DM   \n",
              "3  @martinkanini Hello Martin, please DM https://...   \n",
              "4  @Safaricom_Care am getting some OTP several of...   \n",
              "\n",
              "                                              Tweets  \\\n",
              "0  [waxspel, safaricom_car, askpayp, sponyo, alif...   \n",
              "1  [safaricom_car, safaricomplc, offici, roam, ra...   \n",
              "2                         [safaricom_car, check, dm]   \n",
              "3  [martinkanini, hello, martin, pleas, dm, scree...   \n",
              "4  [safaricom_car, get, otp, sever, time, night, ...   \n",
              "\n",
              "                                    stop_words  \\\n",
              "0                                           []   \n",
              "1                          [what, are, of, is]   \n",
              "2                                           []   \n",
              "3                   [a, of, the, for, to, and]   \n",
              "4  [am, some, of, them, at, this, of, the, my]   \n",
              "\n",
              "                                         tweet_punct  tweets_sent_compound  \\\n",
              "0  waxspel safaricom_car askpayp sponyo alifinya ...                0.0000   \n",
              "1  safaricom_car safaricomplc offici roam rate ma...                0.0000   \n",
              "2                             safaricom_car check dm                0.0000   \n",
              "3  martinkanini hello martin pleas dm screenshot ...                0.3182   \n",
              "4  safaricom_car get otp sever time night is numb...                0.4939   \n",
              "\n",
              "   tweets_sent_pos  tweets_sent_neg  label  \n",
              "0            0.000              0.0      1  \n",
              "1            0.000              0.0      1  \n",
              "2            0.000              0.0      1  \n",
              "3            0.119              0.0      2  \n",
              "4            0.208              0.0      2  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>User</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>stop_words</th>\n",
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              "      <th>tweets_sent_compound</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022-10-23 23:37:49+00:00</td>\n",
              "      <td>samsabwa</td>\n",
              "      <td>@waxspell @Safaricom_Care @AskPayPal sponyo al...</td>\n",
              "      <td>[waxspel, safaricom_car, askpayp, sponyo, alif...</td>\n",
              "      <td>[]</td>\n",
              "      <td>waxspel safaricom_car askpayp sponyo alifinya ...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-10-23 23:36:25+00:00</td>\n",
              "      <td>zac124</td>\n",
              "      <td>@Safaricom_Care @SafaricomPLC \\n\\nwhat are off...</td>\n",
              "      <td>[safaricom_car, safaricomplc, offici, roam, ra...</td>\n",
              "      <td>[what, are, of, is]</td>\n",
              "      <td>safaricom_car safaricomplc offici roam rate ma...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-10-23 23:19:35+00:00</td>\n",
              "      <td>tonui_ke</td>\n",
              "      <td>@Safaricom_Care check DM</td>\n",
              "      <td>[safaricom_car, check, dm]</td>\n",
              "      <td>[]</td>\n",
              "      <td>safaricom_car check dm</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-10-23 22:14:19+00:00</td>\n",
              "      <td>Safaricom_Care</td>\n",
              "      <td>@martinkanini Hello Martin, please DM https://...</td>\n",
              "      <td>[martinkanini, hello, martin, pleas, dm, scree...</td>\n",
              "      <td>[a, of, the, for, to, and]</td>\n",
              "      <td>martinkanini hello martin pleas dm screenshot ...</td>\n",
              "      <td>0.3182</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-10-23 22:01:01+00:00</td>\n",
              "      <td>martinkanini</td>\n",
              "      <td>@Safaricom_Care am getting some OTP several of...</td>\n",
              "      <td>[safaricom_car, get, otp, sever, time, night, ...</td>\n",
              "      <td>[am, some, of, them, at, this, of, the, my]</td>\n",
              "      <td>safaricom_car get otp sever time night is numb...</td>\n",
              "      <td>0.4939</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "cv = CountVectorizer(binary=True)\n",
        "cv.fit(tweets_list)\n",
        "X = cv.transform(tweets_list)\n",
        "y = tweets_df[\"label\"].values"
      ],
      "metadata": {
        "id": "iydfG0TWcDKI"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Plotting the Label Results"
      ],
      "metadata": {
        "id": "YjERuOJJRCbp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [10, 8]\n",
        "for index, Tweets in enumerate(df.index):\n",
        " x = tweets_df.tweets_sent_pos.loc[Tweets]\n",
        " y = tweets_df.tweets_sent_neg.loc[Tweets]\n",
        " plt.scatter(x, y, color='Blue')\n",
        " \n",
        " \n",
        "plt.title('Safaricom Tweets Sentiment Analysis', fontsize = 20)\n",
        "plt.xlabel('← Negative — — — Neutral — — — Positive →', fontsize=15)\n",
        "plt.ylabel('← Facts — — — — — — — Opinions →', fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "Px8b-jdtRFNo",
        "outputId": "a6b8da2d-c40a-43a9-c0cb-db6ca68399d2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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qDjv3QXDOfV++n9ImM/uTqLl7FTP7uShlTBOVzzP67CzKB3WLzrnnZC3yudvWyyfIruuq6OdpqTLU+u5xzn1d/u/s8ZKeL//PyjvT+5nZIy27GjD+XOV+ttv628Dg0JSJOh4i3/n1u2b2CfnRPpIfUfY4+WlQ/lO+T1nsdfJD2b9oZv9Xvk/GqfJB2bvkc3OFepd8aoYXR82mX5T/In+8fF6hNm9eL5D/b/L58jmK3i/fJHuCfM3SGfL/pSva55OS/srMHi3fZBLnMTsk6VnOuRvUU86568zsS/Ij7KTVwden5b+87yrfp+Z/Us+9zcyeIJ8y5D1m9in5VBEH5K/BKfJpBu6mlZvAn0av9XxJv25ml8j357urfN+zUyXtkE/kKefcjWb2WUm/bGbLkr4mfzO+UH7QyCfM7Ar5ARN75dM4PEq+Q/qF6draHH8g6dVm9kX59+9a+SD1gfId/W+X9PxUs89To2v1D2b2Qvl8WT+U78R9P/nPz8MkfT/g9fNcLJ+g+S3R388N8rM3DDKj/Qvk35fXSHp69Lf/Pfl0KPeWf4/P0sr3QR0flv9b+bNoAMoPJMk59/8WPOfJ8n033+WcK5ol5K3yA3C2yudzq6OL755d8vnMfkbS3znnbs7Y598l3Whmn5EPDk2+9vIU+c/7hwqOfwe187eBQRn2sFCW0Vvkb7S/K/9lcbl8R/Bb5Tu4XyT/5TeV8bxnaiVj+HXR8++r/OHk5ys/j9k95P+Dv0a+ieIr8kks10TP+Uhq/8zXSGxfUE4qBvlh+DskfUk+qLhBPlh4vVJ5heT7o50j/+V3a3Se/6GMzPdKZIDP2HZatO3VVcoa8N7Fz70qZ/tro+1fydgW52n754Lj31XSn8vXKB2Qzx/1dfkg/WlKpcaQv8E8XT7wuD66ZtfIJz19haLs9In9f17+5rhf/gbuous4E73/75UP0n4iH1R9Rj7wWxt4fR4un4zzE9Fxbok+r5crSs6a87yjovJeGp3zzfIBynvkA4F1iX1z3/do+2Gf32j9i+VnFrgl/R6qJPN/1c9QQRnWygdon5Lvd3ZLdJ0ulq8pngt5/Wj7VVmfw+hzEud/c+nzytj/k9F+ZwS8v5dH+/5i9Ph85X/HZJZf7X/3TEefVadEHsHUPs+X/768Uv7v6nr5oPCPJR2V2nfVZ0Et/W2wDG6x6I0DAAADFjWDXiGf/qLrPnwYAfQxAwBgeP5QvuZ4YiZaRzFqzAAAGCAz2yDfN/Ek+dxiX5L0QNfC3LcYfXT+BwBgsE6Unxf1gPxo7m0EZYhRYwYAANAT9DEDAADoibFoyjzmmGPcwsLCsIsBAABQ6tJLL73OOXds1raxCMwWFha0Z0+T6c8AAAAGw8z25m2jKRMAAKAnBhqYmdm5ZvZ9M/tyznYzszeY2RVm9iUze+AgywcAADBMg64xO1/SYwq2P1Y+r8tJ8tOYnDOAMgEAAPTCQAMz59zH5Of4ynOmpLc77zOSjjazuw2mdAAAAMPVtz5md5f0rcTjq6N1AAAAY69vgVkwM9tqZnvMbM+111477OIAAAA01rfA7BpJ90g8Pj5adxjn3G7n3Cbn3KZjj81MBQIAADBS+haYXSjpGdHozIdK+pFz7jvDLhQAAMAgDDTBrJn9k6TTJB1jZldLepWkGUlyzr1J0kWSTpd0hfzkrs8aZPkAAACGaaCBmXPurJLtTtLvDqg4AAAAvdK3pkwAAICJRWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYFZieVlaWJCmpvzP5eVhlwgAAIyrgabLGDXLy9LWrdKBA/7x3r3+sSQtLg6vXAAAYDxRY1Zgx46VoCx24IBfDwAA0DYCswL79lVbDwAA0ASBWYENG6qtBwAAaILArMDOndLs7Op1s7N+PQAAQNsIzAosLkq7d0vz85KZ/7l7Nx3/AQBANxiVWWJxkUAMAAAMBjVmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgBgAA0BMEZgAAAD1BYAYAANATBGYAAAA9QWAGAADQEwRmAAAAPUFgVmL7dmnNGsnM/9y+fdglAgAA42rNsAvQZ9u3S+ecs/L44MGVx7t2DadMAABgfFFjVmD37mrrAQAAmiAwK3DwYLX1AAAATVQOzMxsi5l90cx+oYsC9cn0dLX1AAAATVQKzMzskZL+U9L9JV1iZvfspFQ9sXVrtfUAAABNBAdmZvYISRdKep8kJ+nTkj5sZid1VLah27VL2rZtpYZseto/puM/AADoQpUas1lJb5D0iujxkyW9V9KRbReqT3btkm6/XXLO/yQoAwAAXQlOl+Gce6+k95rZvaLHt0t6TlcFAwAAmDSMygQAAOgJAjMAAICeGHhgZmaPMbPLzewKM3tZxvYNZvbhKCXHl8zs9EGXEQAAYBgGGpiZ2bSkN0p6rKSNks4ys42p3V4p6QLn3AMkPUUS3e0BAMBEGHSN2YMlXeGcu9I5d6ukd0o6M7WPk3TH6Pc7Sfr2AMsHAAAwNHUnMbeaz7u7pG8lHl8t6SGpfV4t6QNm9nuS1knaUvO1AAAARkqdGrNvSDqh7YIknCXpfOfc8ZJOl/QOMzusnGa21cz2mNmea6+9tsPiAAAADEblwMw5d7tzbm/N17tG0j0Sj4+P1iX9jqQLotf6tHwC22MyyrHbObfJObfp2GOPrVkcAACA/hh0H7PPSzrJzE4ws7XynfsvTO2zT9JmSTKze8sHZlSJAQCAsTfQwCyaLeAFkt4v6avyoy+/YmavMbMzot1eIum5Zvbfkv5J0jOdc26Q5QQAABiGup3/a3POXSTpotS6/5P4/TJJpw66XAAAAMNG5n8AAICeCKoxM7M1kqadc7ck1j1aPknsx5xzX+iofAAAABMjtCnznyX9SNKzJcnMXijp9ZJukTRtZk9wzr27myICAABMhtCmzIdqdb+wP5L0WufcHSS9VdKOtgsGAAAwaUIDszlJ35UkM7uvpOMkvSna9i/yTZoAAABoIDQw+56khej3x0ja65z7RvT4DpIOtVyu3lhelhYWpKkp/3N5edglAgAA4yq0j9m/SPoLM7u/pGdJ+vvEtgdI+nrbBeuD5WVp61bpwAH/eO9e/1iSFheHVy4AADCeQmvMXibpzZJ+QdI5kv4sse1B8oMDxs6OHStBWezAAb8eAACgbUE1ZlHG/tfkbHtCqyXqkX37qq0HAABoonKCWTObNrPZ9NJF4YZtw4Zq6wEAAJoICszM7I5m9vdm9m353GU3ZCxjZ+dOaTYVcs7O+vUAAABtC+38/2ZJj5fPWXaZpFs7K1GPxB38d+zwzZcbNvigjI7/AACgC6GB2a9J+gPn3Fu7LEwfLS4SiAEAgMEI7WN2k6SruywIAADApAsNzF4rabuZVR4sAAAAgDChTZl3l3R/SZeb2Ycl/TC13TnnXtpqyQAAACZMaGD2JPlpl9ZIelTGdieJwAwAAKCB0ASzJ3RdEAAAgElHnzEAAICeCA7MzOxEMzvHzP7HzK6Jfu4ysxO7LCAAAMCkCGrKNLMHSfqwpJ9Ierek70n6GUlPlLRoZo90zn2hs1ICAABMgNDO/38t6YuSHuucOxCvjObIvCja/qvtFw8AAGByhDZlPljSXyaDMkmKHv+1pIe0XTAAAIBJExqY3SxpLmfbXeSbOAEAANBAaGD2Hkl/bmYPT66MHv+ZpHe1XTAAAIBJE9rH7MWS/lPSR83s+5K+L+mu0fJpSS/ppngAAACTIzTB7H5JDzezx0g6RdLdJH1H0medcx/osHwAAAATI7TGTJLknHufpPd1VBYAAICJlhuYmdlsPAozSotRKD1iEwAAANUU1ZjdYGYPc859TtKN8hOVF5lur1gAAACTpygwe7akbyR+LwvMxtL27dLu3dLBg9L0tLR1q7Rr17BLBQAAxlFuYOace1vi9/MHUpqe2b5dOueclccHD648JjgDAABtC57EfBLt3l1tPQAAQBOhk5jPSHqRpCdIOl7Skel9nHN3bbdow3fwYLX1AAAATYSmy3idpOdJerekD0u6tbMS9cj0dHYQNs0wBwAA0IHQwOy3JL3MOffaLgvTN1u3ru5jllwPAADQttDAzCR9qcuC9FHcwZ9RmQAAYBBCO/+/RdJZXRakr049VTr+eMnM/zz11GGXCAAAjKvQGrPvSVo0sw9L+qCkH6a2O+dcRqPfaFte9jVkB6I5DfbuXWnGXFwcXrkAAMB4MufK88aa2aGSXZxzbmhd4jdt2uT27NnT+nEXFnwwljY/L111VesvBwAAJoCZXeqc25S1LajGzDk3kfnO9u2rth4AAKCJiQy4Qm3YUG09AABAE7k1Zma2UdI3nHO3RL8Xcs5d1mrJemDnztV9zCRpdtavBwAAaFtRU+aXJT1U0uei3/M6o1m0bezSrsYd/Hfs8M2XGzb4oIyO/wAAoAtFgdkjJV2W+H0iLS4SiAEAgMHIDcyccx/N+h0AAADdCM1jJkkys3tJOkXS3SR9R9Ie59z/dlEwAACASRMUmJnZHeWz/z9RfiTnjZLWSzpkZv8m6TnOuR93VkoAAIAJEJouY5ekR0t6hqR1zrk7Slon6WxJj4q2AwAAoIHQpswzJf2Bc+4f4xXOuZslLZvZrKS/6aJwAAAAkyS0xuxG+T5lWb4t6aZ2igMAADC5QgOzN0r6QzO7Q3JlVFv2h6IpEwAAoLHQpsw7STpJ0rfM7IOSvi/prvL9y26WtMfM/jLa1znnXtp6SQEAAMZcaGD2JEm3RctDE+tvSGyPOUkEZgAAABUFBWbOuRO6LggAAMCkC+1jBgAAgI6V1piZ2d0k/Y6kX5F092j1NZI+Kulc51zeaE0AAABUUFhjZmZPkXS5pFdJOkbS16LlmGjd5Wb25K4LCQAAMAlya8zM7FRJ75D0r5J2OOeuTG0/QdJOSUtmts859+lOSwoAADDmipoyXyHpIufcWVkbnXPflPRUM1sn6ZWSHtdB+QAAACZGUVPmwyS9NeAYb9XqFBoAAACooSgwO1LSjwOOcUO0LwAAABooCsy+JumRAcc4TdLXWykNAADABCsKzM6Xnx/zUXk7mNkWSS+RdF7L5QIAAJg4RZ3//07SFknvM7NLJP2npL3RtnlJZ0jaLOmiaF8AAAA0kBuYOecOmtkZkl4YLW9I7XKVpBdL+jvn3KHOSggAADAhCjP/RwHX6yW93syOVyLzv3Pu6q4LBwAAMEmCJjGXpCgQIxgDAADoCJOYAwAA9ASBGQAAQE8QmAEAAPQEgRkAAEBP1ArMzOwZZnbntgsDAAAwySoHZmY2LZ/p/4Q6L2hmjzGzy83sCjN7Wc4+v21ml5nZV8zsH+u8DgAAwKgJTpeRYrWe5IO6N0p6lHzqjc+b2YXOucsS+5wk6eWSTnXO/cDM7lqzjAAAACNl0H3MHizpCufclc65WyW9U9KZqX2eK+mNzrkfSJJz7vsDLiMAAMBQDDowu7ukbyUeX62V2QRi95R0TzP7pJl9xswek3UgM9tqZnvMbM+1117bUXEBAAAGp4+jMtdIOknSaZLOkvQWMzs6vZNzbrdzbpNzbtOxxx7bWWGWl6WFBWlqyv9cXu7spQAAwISr28esrmsk3SPx+PhoXdLVkj7rnLtN0jfN7GvygdrnB1PEFcvL0tat0oED/vHevf6xJC0uDro0AABg3A26xuzzkk4ysxPMbK2kp0i6MLXPf8jXlsnMjpFv2rxykIWM7dixEpTFDhzw6wEAANo20MDMOXe7pBdIer+kr0q6wDn3FTN7jZmdEe32fkn7zZxXMuEAACAASURBVOwySR+W9EfOuf2DLGds375q6wEAAJoYdFOmnHMXSboote7/JH53kl4cLUO1YYNvvsxaDwAA0LY+dv7vjZ07pdnZ1etmZ/16AACAthGYFVhclHbvlubnJTP/c/duOv4DAIBuDLwpc9QsLhKIAQCAwaDGDAAAoCcq15g55w6a2QmSvt1BeQAAACZWraZM51zGWEUAAAA0QVMmAABATxCYAQAA9ASBGQAAQE8QmAEAAPRE5cDMvHPNjImJAAAAWlSnxmxK0tmSjmm5LAAAABOtblOmtVoKAAAA0McMAACgLwjMSmzZ4icwj5ctW4ZdIgAAMK4IzAps2SJdfPHqdRdfTHAGAAC6QWBWIB2Ula0HAABogsAMAACgJwjMAAAAeoLArMDmzdXWAwAANEFgVuBDHzo8CNu82a8HAABo25phF6DvCMIAAMCgUGMGAADQE3UCMydpr6RbWi4LAADARKvclOmcOyTphA7KAgAAMNFoygQAAOgJAjMAAICeIDADAADoCQIzAACAniAwK7F9u7RmjWTmf27fPuwSAQCAcVU5MDOzKTO70sxO7qJAfbJ9u3TOOdLBg/7xwYP+McEZAADoQp0aM5O0IOmIdovSP29+c7X1AAAATdCUWeDQoWrrAQAAmiAwAwAA6AkCswLr1lVbDwAA0ASBWYE3v9mPxkwyo48ZAADoBoFZiTVrih8DAAC0hcCswI4d0m23rV53221+PQAAQNsIzArs3VttPQAAQBMEZgWmp6utBwAAaILArECc8T90PQAAQBMEZgXm56utx3hbXpYWFqSpKf9zeXnYJQIAjBsCswI7d0qzs6vXzc769Zgsy8vS1q2+f6Fz/ufWrQRnAIB21QnMDkl6m6TrWi5L7ywuSrt3+xoyM/9z926/HpNlxw7pwIHV6w4cYIQuAKBdlQMz5z3LObeviwL1zXnnra4lOe+8YZeo/8axyW9fzqc9bz0AAHXQlFlgyxbp4otXr7v4Yr8e2ca1yW/DhmrrAQCog8CsQDooK1uP8W3yo78hAGAQCMzQqnFt8qO/IQBgEJj5Ea3asCF7ZoRxaPJbXCQQAwB0q1aNmZltMLOxD+o2b662HjT5AQDQROXAzMymJX1T0v3aL06/fOhDhwdhmzf79chGkx8AAPXVrfWyVkvRYwRh1dHkBwBAPXT+LzGOObkAAEA/jX0/sSbinFxx+oc4J5dEjRAAAGgfNWYFxjUnFwAA6CcCswLjmpMLAAD0E4FZgbvcpdp6AACAJgjMAAAAeoLArMD111dbDwAA0ASBWYG1a6utBwAAaILArMAtt1RbDwAA0ASBGQAAQE8QmAEAAPRE5cDMOXdQ0rPkJzIfa/QxAwAAg1RrSibn3NvaLkgf3XprtfUAAABN0JRZYHq62noAAIAmCMwKHDxYbT0AAEATBGYFqDEDAACDRGBWgBozAAAwSLUCMzN7hpndue3C9M3cXLX1AAAATVQOzMxsWtJ5kk5ovzgAAACTq25TprVaip7av7/aegAAgCYG3sfMzB5jZpeb2RVm9rKC/Z5oZs7MNg2yfElTOVcnbz0AAEATAw0xombQN0p6rKSNks4ys40Z+x0l6UWSPjvI8qUdOlRtPYDRsbwsLSz4f7QWFvxjABi2Qdf9PFjSFc65K51zt0p6p6QzM/b7U0l/IekngywcgMmwvCxt3Srt3Ss5539u3UpwBmD4Bh2Y3V3StxKPr47W/ZSZPVDSPZxz7xlkwbJYTk+6vPUARsOOHdKBA6vXHTjg1wPAMPWqt5SZTUn6G0kvCdh3q5ntMbM91157bSflca7aegCjYd++ausBYFAGHZhdI+keicfHR+tiR0m6j6SPmNlVkh4q6cKsAQDOud3OuU3OuU3HHntsh0UGMG42bKi2HgAGZdCB2eclnWRmJ5jZWklPkXRhvNE59yPn3DHOuQXn3IKkz0g6wzm3Z8DlBDDGdu6UZmdXr5ud9esBYJgGGpg5526X9AJJ75f0VUkXOOe+YmavMbMzBlkWAJNrcVHavVuan/d9Rufn/ePFxWGXDMCkM1exw1SU8uI2SZucc1/opFQVbdq0ye3Z036lWlEnf/qZTZblZd8xfN8+39y1cyc3cQBAPWZ2qXMuM0/rmkEXZpSYZQdgjMqcLHFqhXgUX5xaQSI4AwC0q1ejMvuGUZmQSK0AABicyoGZc+6g/ATm/9N+cYDBCc38TmoFAMCg1Koxc87tdc7d1nZhgEGpkvmd1AoAgEGhKRMTqUrzJKkVAACDQmCGiVSleXJQqRWYVBsAwKhMTKQNG3zzZdb6LIuL3Y7AZOQnAECixgwTqm/Nk4z8BABINQIz8841M7o+Y2T1LfM7Iz8BAFK9GrMpSWdLOqblsvTO5s3V1k+CceoHtbgoXXWVdOiQ/znMJkNGfgIApPpNmROR+/5DH5KOPnr1uqOP9usnUZUUE6imb02rAIDhoI9ZgS1bpB/+cPW6H/7Qr59E9IPqRjwP54ED0vS0XzfsplUAwHAwKrPAxRdXWz/u6AfVvvRozIMHV2rKCMoAYPJQY4Zg9INqH7WQAIAkAjMEox9U+8pqIcdpsAUAoByBGYL1LcVEn9QNoIpqIesOtiCYA4DRZc65ak8wm5Z0m6RNzrkvdFKqijZt2uT27NnT+nHXr5duuunw9evWSTfe2PrLYUSl+4lJviYxJGgteu6OHdmzE8zP+/QebZcFADAYZnapc25T5jYCs3xWkBSk4mXDGFtYqB5AJcWjMvft8zVlccf/qansz5mZz73WRVkAAN0rCswYlQk01HS0at48nFXn82yjLACA4aKPGdBQV6NV6wy2YOQsAIw2AjOgoa5Gq9YZbMHIWQAYbXUCMydpr6RbWi4LMDRNRjJ2OVq16nyejJwFgNFWufN/H9H5H00wkhEAMEhFnf9pyiwQz1sYuh6jiez7AIC+IDArcPBgtfUYTVVGMpK8tdgkXp9JPGcA3SEwKzA/X209VozSzSp0JGPdTPyTYhKvzySeM4BuEZgVOP30auvhjdrNKnQkI02exSbx+kziOQPoFp3/C5BFvZ5RvG552feT6mTinySTeH0m8ZwBNNdq538zmzKzK83s5OZF6zeyqNczitctJC0FyVuLTeL1mcRzBtCtOk2ZJmlB0hHtFqV/+NKtZ1yv27gkb92+XVqzxtfqrFnjH7dhXK5PFZN4zgC6RR+zAvQxq2dcb1bjkLx1+3bpnHNWRhYfPOgftxGcjcP1qWoSzxlAtyr3MTOzaUm3SdrknPtCJ6WqqKs+ZuvXSzfddPj6deukG29s/eXGSkifLQzemjXZ6V6mp6Xbb292bN5zAAhT1MdszaALM0qygrKi9VixuMhNuY+6yM23vCy96EXS/v0r6+KRuBKfAwCogqZMoAVt5W3rOv9b27NZxKlRkkFZLDRtxCjlvAOArhGY9dio3OwnXVt52waR/y2uxSpbv2WL7zMVL1u2ZD8vK49XUtlI3FHLeQcAnXPOVVokTUs6JOmBVZ/b1fKgBz3IdcHfKrKXri0tOTc7u/o1Z2f9+mEcB/nm57M/I/PzwzlOmW3bnJue9seenvaPkzZvzi7H5s2HH8us+O9kft5/1ubn/b7x40GfMwD0iaQ9LiemofN/AbP8bV3n5W0rSesoJnsdNW0lGe1LstIqn/u8z5fkR+Kefbb0tretrlWbnV0ZudiXcwaAQWo1wewkmZurtr5NbSVpHcVkr6Omrbxto5j/LSs1iuT/Rnbvli66qHjKolE8ZwDoEoFZgb/928NrD8z8+q5N8s1+1LSVt20U879l5fFaWpKuu85vK/vHYBTPGQC6RGBW4LzzDm9mcc6v79ok3+xHTVtJRvuSrHTz5mrri6azKvvHoC/nDAC9kdf5LG8Rnf8H0vnfueJO08M4ziSbtGuYHgCQ1fE/BINPAOBwarnzv0k6V9KrnHO96Kk0jp3/0R9xSoe8DuwoxowAALBaq53/o2DvWX0JyoCuZeXqCk2e2pZkLrpjjvHLqOSlK2rq7Kumuf+6migewPijj1mBI46oth7jadgjW9NJWPfv94uboISsg0yS3DTpbZcTxQMYfwRmBW65pdr6KkY9G/+ol7+KYY9sLcuuP+jau0Eb9OwATWtId++uth4AkgjMhmDUp6Hpc/m7CBiHPbI1pGZunPPSDbopuWkNaRcTxQOYHARmQ9CHPktN9LX8XQWMw07pEFIz12Xt3bBrRwfdlNy0hrTtieIBTJZGgZmZHd1WQSbJsPssNdXX8jcNGIsCkGF2YM/Lrh8rqr1rGlT1oXY0LyCamuomWGxaQxo6UTwAZMrLo5FcJG2T9MeJx78o6WpJByVdKun4kON0tYxaHrNRn7i5r+XPm1DbrPy5fc+3lcyjNjfnl7KcannntG1beE62PrzXWeeRXtp+r5rmrSubKB7AZFNBHrPQwOwySc9PPP6YpM9JeoqkL0paCjlOV0tXgVn8xZpepqebHbfvQUCZvpa/SRDRhwCkbXnnlA5gi967JsFuVUXBUHJb3t/lKL9XACZLUWAW2pS5QdLlkmRmx0o6NapBe6ekP5X0q83r7vqnq068w+6z1FRfy3/66Yevm5kJa4Lqa/NsE3lld6nkyEXNvYMakVrWZJpsSj50KPsYXb1XVZuDyWEGoInQwOwWSWuj3x8p6YCkj0ePr5c0ln3NuuzEO4pJN5P6VP7lZZ9w9ZxzDt+WDkLyDDslRheqlD0vqBnUiNQq/QMH+V5V7WNHDjMATYUGZp+T9LtmdrKkF0p6n3Murjc6UdK3uyjcsHU97D30P/Fhj4rrs/jGuX9/9vbbbw/r/D/slBhdyDqnvGnG8oKaQdWOVqmxHOR7VXVACTnMADSW18aZXCSdLOkb8pOX75V0z8S290t6W8hxulq66mPWZb+jkH5aS0u+k3fbHZ3HaULuvPeoTn+ocbousfQ5bds2Hv0DB/Ve5fWxk7Jfs6sBQwDGi5p2/v/pztKc5Cc+T6y7r6Rjqhyn7aWrwGxpybk1a1Z/ua5Z085NoOxGVDYSrW5w2FXH/WEFNUU3TjqFZ+tjADpqA0qyyrdtW/6+TQcMARgvjQMzSedKOiFn27ykc0OO09XSVWCW90XbxtD3stFuZTVBVUbFVR3RVvXGPcybakiNGakKmhlUIDeMgLHsNav8g5T3t8VnEEBaG4HZIUkPztn2IEkHQ47T1TJqecycK68xK6sJKqoFSue8Wru2PHiJA706QdYwU00sLTW7VijW15qsNoSe29JS+d+Nc91+XwAYL0WBWZXM/y5n/X0kXVvhOFB5B+aiEWZlmd6To8j275duvbW8PPHr1cmeP8xUE4uL0vOfn9+pXfLXgkET9fR1+q025J3b0562ehTl4qIf9JAl+XfKVEwA2pAbmJnZi8zsSjO7Uj4o+4/4cWL5tnwz53sGVeBxUTbaLW8anrm54lFxWTebMslAr06QNexUE7t2Se94R/ENsCzNQTzyNc49ZTb8YK4Po3H7nt+tyTUqOod0iou8v8cbb1x5TaZiAtCKvKo0SY+S9FeS/lq+KXMpepxcdkp6uqQj8o4ziGUUmzJD1OlzE9IZPu6MnHXcOs2SfWnuCpm6J+s8ip43rGa7vlzTPs+I0PQalfVPTHfYDxklzVRMAEKohT5mr5J0XMi+w1jGNTCrI6QzfNHNq+7Nri8j/eJyhPQJipVds2EEIX0JiPoSIGZpeo1CAvm2XxMAnCsOzMxvL2Zm95B0rHPuCxnbHijpWufctxpX39W0adMmt2fPntaPW9RvKeCyDUXcxyzZnDkzI93xjtL11/vmxZ07s5tCt2/3zaTJBLrz8/n799nCgm++TJuf9zMVJE1NFb+fZvnTAHUlr0zDKMvysm8i37ev+PMzaG1co+Vl36csy/S0T1Dc9msCgJld6pzblLUttPP/OZJyvr70VEm76hQM7cvqu3beedJ11xVPn5SeSiZ2+un9uAmXSc9PuG7d4YF13qCJsr5woX3ltmzxrxkvW7aEPa/Kaw5jiqhBT79V1m8s3p4XTFe5RouL0rZt2duy+ob16X0BMKbyqtKSi6TrJD0+Z9vj5GvMaMocYXk5mEYhMWZRYs9kE2Zef582+pht3pz9/M2b651Tn5sQu1R23mXNj3WvUWjfsEl9XwC0Sy30MTsg6XE52x4n6UDIcbpaCMyaG+VzLUrsGdoPKNk3LT5eWV+5ZL+6Lq7fIPrt9aVvYKysD1dRf8C+JKUFgDJFgVloH7PPSvq6c+6w5kwzW5J0L+fcKa1U4dVAH7N8of2D1qzJnpw9q59NnxT1EUprsx9QVl++PF19Vpr2/co6h9nZbiYpD1XWh4s+XgDGQRt9zP5c0lPN7F/M7HFm9sDo5wWSzpJPm4GeSSebzcvltWVLdlAm9TsHU3x+odrsB1QnX1ybst7bpz1NOuaYlfe3rK9Wk+SxXeVYK+vDRR8vAGMvryotvcjnK7taPqfZwejntySdFXqMrhaaMrOFDO3P6xsl9T8HU0hqkLb7ASX7IoUsGzc2f80sZZNrb9tW3heqbL7WPF32s6rTx4w+XgBGjZo2ZcbMzCTdS9KcpP2SLndVDtARmjKzhTT7jPI5lqW5iMUpP6RmTX/xyNUqstJztKHs3Kens2tBk+WpklIkqe7zQpU10fY1fQcAhGqjKVPST+uJ/tc598noZ89v3ZNt3Jt9ys5jdlZaWloJFkKadYvs3l29jF1NXVR27nlN08nylM3XGks3W2YFZeljN1GWnmOQ6TuSaVjiZdhTdQEYb8GBmZkdZWZPM7PXmNlfppcuC4l6Qm+8g9Rm36Ss84trANNzj7YxGXdesCPlz9PZVRCcN3djlfKUzdcqZfdly6tlnZpqv8/ZsOYLjXPSZeX2qxPUA0CwvDbO5CLp5yR9T9KP5fuXfVfSrfL9zPZLujLkOF0t9DFbkR7Kv21b8dD+tvNvlZWt7f5BoakL6vanSsrrW2Y2nL5PS0vOrVuX//5VLU/Wtczry1aWIqSNcx/0NZ2ZKT6nor6aAFCFWshjdqGkd0u6QxSMPVDStHzW/72STgk5TlcLgZlX90aWDs66CMqcG+48g6GvXXQt8hLZrlnjr3EysJmb80vXua6KzqtKvq28z05ZYGKWH7A2fV+7/rxUCcKaBvUAkNRGYPZdSY+Xb/o8JOmhiW0vlPSpkONE+z9G0uWSrpD0soztL5Z0maQvSbpY0nzZMQnMvL5PsNxGrVVdIUFrSO3h+vXZ+8zNVXuttjQZWZkM2ubmso9TNAI1HrXb1fvaxXGrjqotC0wBoI6iwCy0j9mRkn7snDsk6XpJxyW2fVnS/UMOYmbTkt4o6bGSNko6y8w2pnb7oqRNzrn7SfpXSRPffy20n01e5+uuOqBXNczBCCH9qS6+OPu5yfU33ZS9z/79K+9LG/3ZQtW5pln9xvbvz9734MH8vmznnOM7x3f1vrZ93Lz5YOtI99UcVl84AGMoL2JLLpI+J+np0e8flPQB+WBtRtKy/KwAIcd5mKT3Jx6/XNLLC/Z/gKRPlh13nGvMqtS+DLvGrKzpbGnp8H48MzPNapJC5zgMEfJ+l00J5Fw7NT0h1zIuS/r1suYFTR6vSo1R/Np526enu6shbHLcqrV/VWvKkmUgtxqAqtRCU+aLJb02+v2hkn4k3/n/J5Juk7QYeJwnSXpr4vHTJf19wf5/L+mVOdu2Stojac+GDRs6unCDDcyqdL7OCraGeYPYtu3wACH92ktLzq1du3qftWvrly+vz1dIcJZ1rUPe76L94sCraYDcdCLv9P5Z703IkjxG0X7p469f395nrs68lG0EX+mlqN/lsP8hAjB6agVm8s2Va3K23UPSc+X7l90n7xgZzwsOzCQ9TdJnJB1RdtxxqDGr0/laOnzC7aYTLNd5/tJS/o0/eXNq+waWVwMyPV1e3qxrvWZN9vGOPHL18/P6Y8Xn0TRAbjKRd/o6VAnKpqZWfp+bW13eohGpecHaoKQ/s20FYzMzYa8/zL6TAEZT3cDsoKQHJx6bpLcroDN+wTGDmjIlbZH0VUl3DTnuqAdmS0vFQUbojaTKzT8rAKsbUBTdDJM3p6IAoY66709RCoisMqZrS0KuU5MAuexGX6X2q05NWdb55NVO5i1lwXETRdOINV3qNIcXff6bNq8DGE91A7NDqcBsOlr3wLznlC2S1ki6UtIJktZK+m9JJ6f2eYCkb0g6KfS4oxyYhTRL5dXkZC0htU95gUVZTVCeopt/SI1ZnAesqro1ZkXlDa0BalozWaStGrOQIKRKuous/nyD+BuJtXHOeXnfmjQ7hvwNE5wBSOpNYBYd53RJX4uCrx3RutdIOiP6/UPyyWz/K1ouLDvmKAdmbTa9xIFF268ZHzMvGAkNuEKbPLNkvXZIH7ONG1dv27ix3jXvsgYo61yb9jELeU/j45UF1nlBZ9mggKaOPrrdvw2p24EKZTVnABDrVWDWxTLKgVndpqayG2lRbU7V14yPkXdDy9qWNTKw7JoWBQBZr71t2+oakKmp4qAsXo47rl5gM0ihozKTMzyE9gNLvzdlgWrVUcBSsxqisoEYdZfQa9tEXz4/APqtSWD2+1EN1+nyCWYPSvq9xLqfLnnHGcQyyoFZmzVmcbCSFSSlg7as58/N5QdfZc1roTe7ovOtGgCUjQItulZF/fqylr7WeKRnG0iPeo0/E2VBXkiW/7Q2+gzmfQ6a/h0MKwVL3eZ1AJOlSWAWuhzMO84gllEOzOo2S6VvivEoupDaj6zgLVn7lXUTb2vkWdn5pgOAqrUncb+psuuQV5a8/nxFN+e8a1a3Zib0eVnln5lZPRVUWVCWfs2iz1ta01G2TYKvos9P3aBsaSm/D1pocNYkhQuAyVE3MJuvsuQdZxDLKAdmzvkbQl7H+6xlaiq7ZqSoD1fWzatK0NBmqouQXGDxflUnlg5dkq+R1XcttMakqJm1Tl+mKiM+i97f0GOldZk3r6u0FlLz+V3L/mFI1niV/e20mfQYwHiqFZiN0jLqgVksecONv9jn5sJHZU5PhwV4dfIrtd1pOiQAaPvmHS8bN9Yrc5XzqDuxd0iTcVkNa5NEt3WCreRnLp3/zLn6CW5DlzY674d81upcHwDIQmBW06ADsyxVg5O1a8trmerUcm3btjoB6bp1zW5GITe4spt5nSbgukFZ1SbesqApT1mTccjnIX5/qzQ/p/uqJZtDi97nuomR6y51U7oUCWkuj2vMyPIPoA0EZjX1ITCrU9MwN7dyAynrIB+iq34zZU1CZc11yefXraEKKUtREDnoGrOQYLUsjUlWP766tUBd1WrmvRdtZ9kP7eMZf9bJ8g+gDQRmNfUhMAsdkZh3kwjpS1a2z7BGmuX1McuaX7NJcFH23KIAZ9B9zEKC1arXpEktUBdNlEXarrEqCyzTKVioMQPQBgKzmvoQmBXd+JvWEhUdPzTtRNdC+jAl960zCrJuLVVZ4t0uRmVWCVbj/cuuX51aoDYCsDq1t1UGR4Rc96LAMuta0ccMQBsIzGrqQ2DmXPGNP6sZJr6hhNygQmoAxj03U91+XYOsJUm+l8m+fmXlCQkkqp5fG0HZ+vXN0lrUaXbOEpJeJis4qxNwA0Cs1cBM0vGSPlD1eV0u4x6YFamSaiMrV1nR/vGNJ2/S6HFJA1BnJOQga0lC+0HVzTUWevyQ+TFDj9GVoj5/eTWKdZLrAkATbQdm9xp2Qtn0MsmBmXPVOmBnZfcPCeg2bw7LzdRF813XmjaPdVX2snxlIQFEaDPl0pKvxWoSdIV8jrp+X4v+2ch7/bLrTMd+AG0jMKtpVAKzLnNEZd30swKRvJq7tpKqhmgSIGXlkIt/Fg2aqHvOIeWpEkBXnc4qHcQ1rQlrEjy2qSyQLXr9PjRZA5gMBGY1jUpg1lXKguSS7OieNQ1QeiaCKje2Nm6IWUlMqwZIRcFQVg1alamlqgp5T5PTTxUNiBhknrGy43dd+1T2vhS9/rCbrAFMDgKzmgYdmFWp8UknBC0KjKoGYFnrk7nR6gZ1eefQ9Ca+tFRcaxhae1altqVs36YBSJV8ZWWq9EMMWfIGgyQ/t8OsfVpaqj9imY79AAaBwKymLgKzKiMs826+WbVDMzPtNWlWrRELWUJGz6WXubmwaxoSMIYEMiHXL3TfLmvM6jTTtllLFtJ5f9i1T8N+fQAoQmBWU9uBWdHNokqW9qIO3aHzapbdeJP9rNrqFB6aKLVqYBYakNZtUk1e35Dyd9XHrO5x22rqrjqiMqT2qcsaKmq/APRVK4GZpDtJun8yMJN0qqTp0GN0tYxKYFYUfIUEFxs3lt9kZ2ZW5jmcm8vPeZW3ft26erUroTVqZUlbs/bftu3wjvnJG23VkYt5N+oqqRPKcsi1ITSwSL+XU1Ort1epSQ0ZeduWcazVIhgEEKKtwOylkq6XtCjpoKTfkPQTSWeGHqOrpU+BWdEXc1FNV5sd+MtyVMXpL7KCq7p9kdLnnbdflcm4y5ZkXrbQPF9lQUCVfm99uAkXBd6xKtd6kMZtFOQ4BpoAutFaU6akf4iCsYPRz5dWeX5XS18Cs6wvZrOVmoeiG1Hb/YCSAUNc45R83PZrpYOUvACvrMapzms7l93vrigoKwsChhU0FAV7RdNwFX1GQ6/1oJUN2Bi1WqdxCzQBdKfNwMwknSfpkKRXVHlul0tfArO8L+a4b1LZf9ShGfnLlqKaoaIRa0XL3Fx+zq6sQG/t2uw5HZNNfVUTqOada9G1l4rPt2jEaJu1H3m538oC5vg16+QZK3v9ovWDUPT30tZ1H6Q6c44CmExtd/43SZuqPq/LpS+BWVkNgHPhN8K2grJ0QFSnlioveCxLjVAUzCXPu0lwVrXPWt7zs6QHQdTtc5V13bOaINtOFFynXIOcGzKvhrnq+9SmJudKjRmAUExiXlPVm14bObliGzdWuwmH9FOr0n8suW9Zh/Y6/PxAWQAAIABJREFUfeeSN6u6AUnVUZ5Fz0/r46jIqktZWUNScoRch5DawKLrXKVvYpeavuf0MQMQisCspqKbXpaiVBZV/muu2sSXrMlpIwgwq3aDqTvaNN5e1NQ4N+dHisaP45qmOnnRsgKPPGUBZZWalUFMmZW1lAUFIUlsk9c+6/plBftZzdjxazW97l1q47X7MCAEQP8RmNVUdNPK03RqoCoBRlbz2tJS8xt6Xt6yvBtUnfxsIUFBUZ+rvGufbHosCirKFNUCVq0Z6brGLE6NUvVcm5ar7uCNQdVUVkUfMQCDQmBWU9HNpUgX/VSq3CzybtJzc+U3U7N6N6iizuWh/YiScz8WjUDMCjbyaivTx1i7Nuz9KKo9qVqz0vaI2ypL1bkhQ5c6A0hCrlVcrmHUOlV5X+uUkdo0ADECs5rqBmZNhDZ7TU/Xq3UIaSYNvUElbzRxR/+sm07yNUNGSIYEDOnXDw0IZmbCRiQWXcPQwLVqP8EmwVedACg+z0GUsexa9cHS0uFNsMnPS3K/qrV69D8DkERgVlNZYNCFKs1LZU1CRekRim7kITeosuApq4N4aFb9smswNZXfTBmy5OWNC+nUXlS+on5XbSxZk9UXBWWhN/62m1pDZoHo40jFpaXDy55Vw1qnLxojNgEkEZjVVHRjaVOy2S5rvsu1a+sNKgjJk5V1I19ays8oPzcXPndmsmxlN/9kENF1Z/nQ0aJF1zUrqMu7ZnWWvKAqdBSjdHhi4So1rGZ+doiqTZ3xCN6y/bqY7qlpU2HoZ6JOUz/91wAkEZjVVHRjaUte4tD161ffYKp+sVfp2xXffOIbWVuTlifLFvraznXfWb6o+TP0RtlFzVjWPKBlZSjKW1fWnB2S3iLkPLMCobL3MLSvX1yGsoCrjabC0M8ENWYAmiIwq6noxtKWvD5X09Or98v7Yk92mK8b3KRvPG0GQGXlyetY3VVn+bLRokU3yq76jNXtZ5QXMBWN0iwK2PIUfZaSn9N0n8OyJs2iPotV8qgVlbFK4FOlbyV9zAA0QWBWU9FNpYq8//jLptlJH6MsWKnbHJi+8bQZABWVP6SPXHzzr1OGzZurjRYtKk+XHfnr5MkqO2bV5uCiMpQdK+96Zk3JlS5j2ecjNA1IlRrZomsb+plgVCaAJgjMaiq7GYXI+7LfvLn4+Okas/hY8Rd7USqJpaX87Vk3sLm51f2R6gYY69aVzwWZDrayRnMmn7duXfW+WyE1Eekmurhv1HHH1T//ukuV5tOQmsSilB51ylA2WKRon6KgukofxLLyVunDWHaNCZ4AdI3ArKaiL/pQdftLlXWOLhuNV3TsNjupp5c4wMkLItatCxu1V2VZty4/VUeWvGD56KO7uy5FS2iNWehnKe/6z8zkv/dlg0iy3rOpqbAa2qyas3Qfs6Y1fFVG/QLAsBGY1VT0JR+qTg1UyIi1OjUUdZeq6SmKmqDaXtJ9nELm+Ox6cEG83OEOq5ses4KiKilPQl83OXl8smYyLyAOqU3KanZPBld55cuayD7rfSl6ftXmxbzrwghIAH1BYFZT0c0vVJ0gIERerU+bgUXV5qJhLnNz2UFp1ui/rtJxVJ3HM92EXNYHrslk73mBclGi4pD3Pq6FyquhywoGswKzvP6W27ZVb15kBCSAviMwq6noZheqzg029Ca0tJQ9wXebwU6dGps+LzMzg6nNS9bq1KkNyntO+rPTNMgMrUUKSSWRDqCKrnO61isk8KvydxGSwR8AhqUoMJsSOrW4KO3eLc3P+8dm/lZRZO9ev8/evdLWrdLycvZ+550n3XTTyuNDh9opsyTNzEg33LC6LGbhz1+3Tpqdba88bbntNmn//u5f58ABaccO//u+fdn77N/v98t6Xt5znPOfJTP/8x3vqPa+pG3Y0Gy/5PrFRWnnTr9u377i65y8PlL++e7b5z//W7eG/11Ih1+TJtcIAAYqL2IbpaXPNWZJdWud0jVXcefuLmt88pLMhtbQxM1Vg+pr1telzntedWaCkNeomnA2LSSVRNX8c8natqLzLapxzCo3TZmDxUhWoDrRlFlP0U2ljrb6Ng2yc33eDa4oiWnypttmAFnWPDbopazpuO77Hd/cigKhdEqRss9LUcb/kHlR02lOsm7AVYPQZDNl1vtaNml81f0nvfN/FwEUiXOBegjMaiq6CdQxDv200rUORRnm2zznZKDX1awAdZa2yxIPVigaYRpyDdI55fKUvX9VbrxVAtFksJg3MKKsBizv80mN2eG6CqC41kA9RYEZfcwGaOfOw/tdzc5Kc3PDKU9Vs7P+HJJ+8pPsfeP1eX2Hqpqa8v2Enva0w/tltalK3zgz6eyzV/oPTk/7n03ez6OO8j+3bl3dR+vmm1d+37Gj/BrkvS9pef3A4vVZr5XuHxbL64c2Py8tLa3uG7d7t++Tlncu69f77VL2302effvy/87Sn90sy8vSwoL/vC0sFPdjGyVV3scqivoGAqgpL2IbpWUUasyKmoPyEngWLVl9hrpYkolb06kdyqaUCp0+qC9LshYnWZNUlsw3XevQpLk1pH9Z1SbSvCbQsvem6LWyRmNmXauyWpnQZsfQEZ91RnEmX2Ncm+W6at6lxgyoRzRl1lN20woV0l+obF7BvAAiefMpm+apzhJ3sM666Yak/di2rXogUfSaVZeNG8Oa/sxWEvtWbS5NTiRfFqw2XZxrNogk9HMWN2WGBEB56WDayDmWF2B1EUSNc5DR1bmNczALdInArKayG2Sosi/FqlndQ0fN9WGpE1jFiVebzmKwcePKtQmtAevzEs9y0PV7neznllWTmxwc0PSGX3RjrzIAoo3O7OM8aKDLAIpRmUB1BGY1Fd28qij7wg8JFIrSGIxSc+Egl/j69CVgzWoKrvreZTUrJyeCr1u2rLlGi8oW79dGMJN3Yx90DVaT1xuF4GQUyghMCgKzmspu+qHaqDHL+hI97rj6N+LpaX9z37ix/jHiG/CwA56i96gvQeu6dfmfjzrXMK+2o275soKPsnIVpW3JOl7VwGDQNVh1a5VozgNQVVFgxqjMAcgbVXbjjX7U186d5ZnJn/1s6ZhjVkaL3f3u0re/Xa0c09PStm3+1nH77dKpp0pf/Wr+viGjC6d6/gnqy+iwI4/M3xaafT8pb0RdPEK0qqzrVFaueJRfyAjIOtn7Q2YbaFNylo706NEiXY14HNcRogBK5EVso7T0vcbMufwkmnGtQFmS0LaWZP+dpn24WMKXrFqeotGMTY5Zp+k2WcNVtVzJptS8mrA6zYSjUhPVRc3eqJw7gHpEU2Y9RTejOsqa1daubS9AK0opMKhUG+O0ZI1I3bw5PM1JOgBpo+9bXlCTbDJMB03btpV3qK9TrrbSYhSdS1/7RXXRF26cR4gCcI7ArKaiG1EdIbUPbdVilZW/78u6dSsBRV9q9rKmMwrNWRY6fVHo8ZrUnhQFO0365BUFDYMINIYVxHVRuzXOI0QBOEdgVlPRTaiOQXVEj3NQ9SWgqbtUnStxENc0VqVmKf1c54rPKSQ/XFdBR5NrXRQ0dN00N+ymv7aDQmrMgPFWFJj1vOv2eIg78e7dW97Jvw379/uBAgcPdv9aXYo7UHfV2buK5BRHy8t+KqaQqaFmZ6W//duV58WdufMGTUxP+1twnvl56aqryjukl8nrWN7kWmc9N36dpz9dusMd/ICSKh3rQ3XVAT/U4qJ/Xw4dauf9aTKtFIARlxexjdLS5xqzoszow6oJmpoazuvWXbL6RQ1jyXs/85bp6Wp9t0KOmzUVUtVamqpJXfPKkfX8kM9+PMNCm8ax6W8U+tcBqEc0ZdZTdpMOUdQk0UZ2+6pLHwKcOmXueqqjkKXo/SwLCvKel5zOKSRZcDzzQ1ZftNCmuyrTIGVN4xS/H2VBQ97rmLUfZND0B2CUFAVm5rePtk2bNrk9e/a0ftyiZsfQy1Z0jNnZsOawtszPS6efLr3pTeHlr8Os/ePHZT/nnHaPG2r9eumGG3zTX+i5xc2OUv7zzHzzVyzO95X3uVi3zh8nb3vyNfOEliVZph07fK6zDRt8c1pIU13RtQopZxVZ1212tt3mUgBoi5ld6pzblLWNPmYdm57O3zbIoEzyCW0vuKDboEzyx19aqp/sNMu+fdKuXT5B7jDccou0fXt4Qt3ZWR9IlvUpS/fLipOc5rnppuLPTUhC3aqJW+v2nyrqr9Z24t+6yWEBoG8IzDrWpw74+/f7ZRCe/vR2jxff5HftKg5265ibKw8ib7vN1zRmvZ+zsz5gTAYFZ58tve1tK5nu856X1Zl7cbF+UBvSeX9QHcuLZrToYkBH2x3wAWAYCMw6ljet0SBGZw6Tcz4oaUM6aNi6td5x1q07/LrHoyavuqr8PcmqaZye9jUzu3atDgouuii7Zmt6OqxGJy94KpomKzS4GlTt0uKi9PznZ19zRhcCQI68zmejtPS5839owtCy5bjj2jtWyDI15TPbdz1YID6nrMz6ceftrI7i27ZVG9WaHHXYZnLVvFF/bYwSzCpr3qjJeFBA0XOHpU9lAYA+EKMy62kjMGsrJcYgR1Om0zx0GRCGBExFQtI7rF0bdryiY+W9j3mj/qqMEqx67mX7DzvZKgCgGIFZTW0EZm1m+x9W/rHNm7vNwJ9OGVEnUCk6ftVAL37P4lQmIXNMZh2nbE7KvInCmwZRpI4AgH4jMKupjcCsjcmq+7DEwVnopN11l5mZ7NdIN9WllQXAs7PObdx4+DlV0VbNVshnokkQNY7JVgFgnBQFZuQxK9BGHjPJ51h62tPC95+fb6/jvOTPY8OG5sd0bmU6omGMNl27VjrqKOn66w/Pp1WW/yvPxo3S9763Mlp1bs4PBuhyRF88PVeRvJxiTY7fdu4wAEA95DGrafPmauuzxMk5q8gbkVdXHMQ0OUZscVE67bT6z29Shltv9QGUcz7w2Lp1ZY7H5EjDKi67bHUKkf37pWc9a+W4XWiSaywE8ywCwOgiMCvwoQ8dHoRt3uzXh4hrcarUVM3P56czCEluOjOz+nF8Qy5LXBpiaspPjn7xxfWePze3+rya5iNLT1Id57Fqmtj2ttu6nfy6LOhqGkSRbBUARhdNmR0KabJKKptCZvv24imJtm2TTj21ePqcYeVPW7tWOvfc1WXJan6cmfFlvPXWsONmNfnVbdYsO25bssoXT2M1Px8+5REAYDTRlDkkRU1W8/OHZ4svq9XYtSu7GdXMH2vXrvLs50XNs9u2NavFMls5l/S5pYMyKbtm57zz/L5FiVSTsmqfso67cWO1c+kiM30sq3zveIcPzMhYDwCTjRqzDnXVCbvupNKxLVtWN0cWNc+GTtpt5oOLNoOK5Hne5S7Sj3/smxljVSepTp/3xo3SFVccXjs3M+MDRAIkAEAXqDEbkq46YTedE/BDH1qdSKGoz1xIzZGZn3qniyl94vO87jofLDXpN5U+76985fDaubk5gjIAwPBQY9axprVbw5bVH6oobQUAAChWVGO2ZtCFmTSLi6MdtMRlH+XgEgCAUUFghlKjHlwCADAqBt7HzMweY2aXm9kVZvayjO1HmNk/R9s/a2YLgy4jAADAMAw0MDOzaUlvlPRYSRslnWVm6UQGvyPpB865n5f0Okl/McgypsUpIJJLVcvLfoRmnFQ165gsLCyTs9z97ivfCVnLMcf4pegYxxzjcxsuLPjR02vWlL/u1FR4GY88cvUMGMvLq8uUfv2jjlo5/po1fhR03jne+c4rz4vPdWpKWr++vFzp/ePv1DVrfHlOPnn1/jMzh8/ksX37yvWKn9emtWtXl2Ht2uJ7w9SU/5k348id73z4dUjuH3qcojKkj3/yyYfvm762Wfu0IfR8mpz3II5XW94kml0skh4m6f2Jxy+X9PLUPu+X9LDo9zWSrpP8IIW8hUnMWVhYWNpfzPx32NKSc2vXDr88Tc/DOee2bcveZ9u2du4bMzPZx5+ZKb83zM6ulDN29NH55zU768sdcpw696eNG1f23bixfJ82hF6X0P3aft22qC+TmJvZkyQ9xjn3nOjx0yU9xDn3gsQ+X472uTp6/I1on+vyjtvnScwXFtqdkBwABime4mzUv8fi/JFr1kgHDx6+fXpauv325q8Tet/Iuzek81wWHU/y5c46n5B8mSH3p7jMbdwPQ4Rel9D92n7dtozlqEwz2yppqyRt6DJNe0MhE1YDQF+Ny3dYfB5ZQUzR+q7kXdeq1zuv3CHH6eN7G3pd2rp+XR2viUF3/r9G0j0Sj4+P1mXuY2ZrJN1J0v70gZxzu51zm5xzm4499tiOittcj2NGACi1YcN4fI/F55A37VyT6ejqyLumVa91XrlDjtPH9zX0urR1/bo6XhODDsw+L+kkMzvBzNZKeoqkC1P7XCjp7Oj3J0m6xA2yvbVlWdn/AWAUmPnvsJ078zuwj4L4PCSfMDtL3vqqZmbC1ofODHP00fmvNTvry113hpmy+1NyjuG8+YarzkNcp0xZ59P2zDpdzdRTS17ns64WSadL+pqkb0jaEa17jaQzot+PlPQvkq6Q9DlJJ5Yds6vO/z4gPHypamnJufl5/9ypqeF0fmVhYenPctxxK98JWcvcnF+KjjE35zt+z8/7zu3T0+WvaxZexiOOWN3xeWlpdZnSr79+/crxp6ed27w5/xyPPnrlefG5mv3/7d1/1F3Tncfx96eJYhCDoNSPmCW0pZZOtatpZ40QZpUgNVTSoSMzrSmla1Qxxihpoz/86jLLqB8tNTP9IWQmnXQYJk2EoVioqhWEUD/S+BFC/Igk6Hf+2PvKeU7uvc99bu5z73mefF5rnfU855x999n37HvO/Z6997knYpNN+i9XOX3tnDpiRCpPeZD6yJFrD+A+4YQ1+6v2uk4q3wBQHvhf/m6Q0t9GA83r3QBQTN9qPs3KUM6/3qD+8r7t9MD/cpn6ez/r8r67kV8zVGXw/2Cp8iOZzMzMzIr8EHMzMzOzIcCBmZmZmVlFODAzMzMzqwgHZmZmZmYV4cDMzMzMrCIcmJmZmZlVhAMzMzMzs4pwYGZmZmZWEQ7MzMzMzCrCgZmZmZlZRTgwMzMzM6sIB2ZmZmZmFeHAzMzMzKwiHJiZmZmZVYQDMzMzM7OKUET0ugzrTNJS4KlB3sxo4MVB3ob1nut5+HMdrx9cz8PfUK7jnSNi63orhkVg1g2S7o2IfXpdDhtcrufhz3W8fnA9D3/DtY7dlWlmZmZWEQ7MzMzMzCrCgVnrrux1AawrXM/Dn+t4/eB6Hv6GZR17jJmZmZlZRbjFzMzMzKwiHJiVSPq0pIWSFkk6o876DSXNyOvvljSm+6W0ddFCHZ8i6SFJv5U0V9LOvSinrZv+6rmQ7ghJIWnY3d013LVSx5KOysfzAkk/7XYZbd21cM7eSdItku7P5+2De1HOTnFXZoGkEcCjwIHAYuAe4HMR8VAhzZeBvSLieElTgMMjYnJPCmwD1mId7wfcHRErJJ0AjHcdDy2t1HNOtxlwA/Be4KSIuLfbZbX2tHgsjwWuA/aPiJclbRMRL/SkwNaWFuv5SuD+iLhM0oeAGyNiTC/K2wluMevr48CiiHgiIlYD1wKTSmkmAf+a/58JTJCkLpbR1k2/dRwRt0TEijx7F7BDl8to666VYxlgOnAesLKbhbOOaKWOjwMujYiXARyUDUmt1HMAo/L/mwNLuli+jnNg1tf7gWcK84vzsrppIuJtYDmwVVdKZ53QSh0XfQH4n0EtkQ2GfutZ0p8CO0bEDd0smHVMK8fybsBuku6QdJekT3etdNYprdTzNOAYSYuBG4GvdKdog2NkrwtgVlWSjgH2AfbtdVmssyS9B/geMLXHRbHBNRIYC4wntXzfJunDEfFKT0tlnfY54JqIuEjSOODfJe0ZEX/odcHa4Razvn4P7FiY3yEvq5tG0khSs+lLXSmddUIrdYykA4B/Ag6LiFVdKpt1Tn/1vBmwJzBf0pPAJ4DZvgFgSGnlWF4MzI6ItyLid6SxSmO7VD7rjFbq+QuksYRExJ3ARqTnaA5JDsz6ugcYK2kXSe8FpgCzS2lmA8fm/48E5oXvoBhK+q1jSR8BriAFZR6TMjQ1reeIWB4RoyNiTB4kfBepvj34f+ho5Xz9c1JrGZJGk7o2n+hmIW2dtVLPTwMTACR9kBSYLe1qKTvIgVlBHjN2EnAz8DBwXUQskPRNSYflZFcBW0laBJwCNLwN36qnxTq+ANgUuF7SbySVTwJWcS3Wsw1hLdbxzcBLkh4CbgFOiwj3cAwhLdbz14DjJD0A/AyYOpQbTPxzGWZmZmYV4RYzMzMzs4pwYGZmZmZWEQ7MzMzMzCrCgZmZmZlZRTgwMzMzM6sIB2Y2JEkaIykkrZa0U2ndIXndmN6UDiSdLml8neUh6aQeFGlYkzQt79ub66ybKWn+IG33KElTO5zn+Pxe9uxkvkOZpCfzPqkd849I+nr+XatObWNqzn/TPL9N/lyNKaVz/digcmBmQ90GwD/0uhB1nE7+YcuSccD13S3KeuUvJH2si9s7Cj/WqVt+Sjp+DiQ9yPoc4NsdzP+GnP+KPL9N3saYUrpf53SPd3DbZu9yYGZD3XzgbyW9r9cFaUVE3BURz/e6HMPUMuBB0qO0KkXSBpJG9LocQ9yz+fi5NSKmAdcAx0tSJzKPiKU5/6bPV4yIV3O6NzuxXbMyB2Y21F0OvA6c2iyRpPdIOkPSIkmrJD0q6dhSGkmaLukFSa9KulrSlHK3qKTvSnpQ0uuSFkv6STEwzM9e3Ao4p9D9Mj6ve7crM3eTPJcfqF0sx8ScbtfCsi9KWpDL/pSk09vbXcNaAN8CDpP04WYJJe0k6VpJyyStkHSzpN0L6+t2V0maL2lm/v8a4Ahg30I9Tyumk/R3kh4HVgLbS/pA3u4zebsLJJ1c/gxYS+4DNiE/E1HS/pLulrRS0vOSvl/rlszrN5B0oaSn83G0RNKsWndosSszH+8P5pfeUqvfnK7PZyPX9Vqt4JIuyNtSnt9I0vm57ldJekDSwYO3e2yo8snAeiIHQWdI2mIds3oDuJh05bxVk3SXAGcBVwITgVnA1ZIOKaQ5GTiTFOwdCbwJnF8nr21IXSgT82v+BJhX+HI9HFhOenzXuDz9uk4+M4BtgX1LyycD90XEIgBJpwGXkZ77d0j+f7o8Vq2e64HHaNJqJmlL4HZgd+B4UnfkJsAvJW08gG1NJz3m537W1PMPC+s/BZxA6mo/lPSZeD+wEPgycDDwA+AbVLM7vurGAKuBZZL2AG4CXiQFy+cAfwXMLKT/R+Bo4Ouk7tCTSXVSryXz2ZwW4ETW1G89M4CDJW1SW5CDsaNIjw+qPV5nJqnb+9ukz8M9wGxJe7f6hoskbSzpLEkbtPN6q7CI8OSpqxPpguAq4BXgI23mMYbUQnIIsHnOa3ped0heNybP7wr8ATi2lMe/Affk/0eQTsaXltLcWMyrTjlGkL5sA/jzwvIXgWl10gdwUmH+AeDywvyGpC+LU/P8KFKL4DmlfL4JPAeM6HV9VmECpgEv5v+nAu8Au+X5mcD8QtrpwEvAloVlW+T9fmKeH5/ras/SduYDMwvzffIupXsT2LZJmQWMJF0MPFFYXnfb6/MEPAlclPfXH+VjfHmtLkhjzh4rHg+kwCiAcXn+v4GLmmxjak6/aZ7fM8+PL6XrUz/A1sDbwJRCmnE5zT55fkKe37eU123A9W3uk12A35Me6P3eXteRp85NbjGzrsqtSlcBnwe+AryZu3fqTTu2kmdELAf+BThJ0qg6SSaQArNZkkbWJmAusLfS2J8dgfeRTnJFaz3AXNJBkn4laTnphLw4r9qtlfKWzACOyOUBOAjYDLguz48jteZcXyr7PFJr2w71MlXquh050KlRISWNaCO/hmOq2inbALr7fgw8TWohqecAYA7wauF9v0bqGtunxW204r4ojSfM3VnfkLQIWAW8Rep+3aXZ/i9rsz4a7r8266Pu2K7cGt6xz17BKXl/vQH8ghTUnJjXfRyYFRHvFNL/B+n4/LM8/xtgqtId03s1Kv9ARcRS0vE4ubB4MvB4RNyb5w8gXUjdUecc1PAzJ2m7RudH0kXcCcD+pHPbhp14P9Z7Dsys2z5KujLdgNRi9XCT6UcDyPfinGe97r3RpJat5aQTe226hnQFvh0pKANYWnptn3mlO/5mk4Kxz5MCp0/k1RsNoLw1M3L59s/zk4E7I+LpQtkBFpTKfkte3ih4PbuUvtWpkblt5DW3SX7tlO3sJvm9KyLeJnVBHyNp5zpJRpP2czn//Wi8P9tR7yaP80jjIa8kdWV+DDg3rxvI5+dxBr7/rq6XkdJ4qnbq49i1c4O8fMD5qf+ft/kxaX/tBYyKiEMLge92lPZ3DtJeArbMi84FLiV1Iz8APCPp7/vZZquuBQ6SNCoHwJ8lHds1o0nnmPL7nkbzz9x3aH6O/C/ShdvBebJhoOUrNLNOiIh7JJ1FGgMyJSL+s0P5vijpCuCrrLmKrllGunL+FKnlrOwF1hwLW5fWlecPJwVrkyOiNhi43pd/q+V+XNK9wGRJt5PGnpxZKjukrpt6X/QLG2R9JanrplO+RGrJG4jXmqxr5yctlgwg7dWkMYX1xm4tIwXX0+usq5V5Zf5b/p2sLUjd1K2IOss+C1wSEe+OXZQ0scX8ig4ltZgMRKNyL6G9+vhdg+W/aDO//ur3+UILVNmzpLGf78ottluRj6GIWEkK7s+WNJY0vvBiSQsj4qY2yls0izT2cxLwFLA9fQOzZaRux88MJNOImEqTn2ORtC/pZz5+FBGzBlRiqywHZtZ1EfEtSe8A10qaEBH/16GsLyQFZceXls8jtZhtHhFz6r1Q0jOkroZJQPFHSg8rJd0YeKsWlGVHs7bVtN4Cci1psPq8nH/xDq87SWOVto+IG1rMj4hYwsACmf7yaxQAtptfoy/YTuVeCkCOAAADK0lEQVS/StKFpBaH++jbGjiXNP5oQTT+yYNa9/QHyTduKHWtf4A0lqlmIPUMqX5X1WZy8DBlAK8HICIe7D9Vy3mtBjpWHxHxEqmlqpvuBg6XdGahO/MvSd9xt5cTR8Rjkk4lnS8+RLpxoGx1/ttv/UbEy5L+l9QS+xTwcET8tpBkLvA14PWIeKTF99RU7s68AfhBRHy1E3laNTgws56IiO9KWgjc1cE8n5V0NWncRXH5QkmXkwLB80lfQhsBe5AGiH8xIt6RdAFwgaSlwB2koKz2swu1lrY5wMmSLia1DHwSOKZOcR4BJkq6iTR4f2FENGpBug64IE+3RcSzhbK/ovQTDP+cW+ZuIw1B2A3YLyIOb3X/rIeuILU+fhK4tbD8e6Q6myfpElJLRu3u2Nsj4mcRsTi3ZE6XtIK0z89kTQtmzSPAJEmfIQVzS3JQ3Mgc4MQ8xmwZKTDw2KB1dy7p7tifS7qMNPbyPODmiLgTQNIsUpB+P+li50jSd+BtDfJ8Oqc7Vmk86Vv9XFDMILXU1sa8Fs0hXfDNkXQeaWjCKGBvYKOIaDQespmFwNSImNlvShtaen33gSdP7UwU7sosLd+ZdKXb505K0h1wJ5NOiKtI3ZG3An9dSnNuXvca8BNSkBfAHxfSnQ48QxqE/EtgLGvfbflRUtD5BoU7u8rpCulvz+u+1OD9HkP6UnkTeJnUQnBKr+uhKhOFuzJLy8/M+3V+afn2pDGMz+fPw5OkMUx7FNLsSrq78g3Sl+Ak1r4rczSpG2tZ3s60vLxPukL6bXP6V/O2zweOo+/dgOPxXZnl/fYkcGE/aSbk42IlaXjC92v7NK8/jXRRtjwf33cDkwrrpxbrIS87Gni0dk5pVj+krv4Ved3udcq3IemnURbl/J4jtdRN7PX+9VStSRH1hkGYGYCkHwIHRkTb48jMzMxa5a5Ms0zpl7wnA78idV0eBPwN/vFPMzPrEreYmWWSdiGNEdmbdAv6U6RxSheFDxQzM+sCB2ZmZmZmFeEfmDUzMzOrCAdmZmZmZhXhwMzMzMysIhyYmZmZmVWEAzMzMzOzinBgZmZmZlYR/w8SqCZL3QDHOAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Plotting on a Pie Chart and Bar Chart\n"
      ],
      "metadata": {
        "id": "iAhja2nkQIT9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "tweets_df['label'].value_counts().plot(kind = 'pie', autopct='%1.0f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "RsCT-84zQcYW",
        "outputId": "456e37fb-7756-468d-9bea-f4b605a4c29d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df['label'].value_counts().sort_index().plot.bar()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "7DZywFkdRoNm",
        "outputId": "da7812ad-88f9-4d47-8372-12c49c7cb669"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Classification using SVM"
      ],
      "metadata": {
        "id": "RIAJIRsDcNxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#encoder = preprocessing.LabelEncoder()\n",
        "#X = tfIdf.fit_transform(df['Text'])\n",
        "#y = df['tweets_sent_compound']\n",
        "#X.shape         "
      ],
      "metadata": {
        "id": "6jiWspD0aLNI"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.2, random_state=0)\n",
        "#encoder = preprocessing.LabelEncoder()\n",
        "#y_train = encoder.fit_transform(y_train)\n",
        "#y_test = encoder.fit_transform(y_test)"
      ],
      "metadata": {
        "id": "R8RMvWsSbSdz"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split( X, y, train_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "3-LJmU0UWkbs"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "epochs = 20\n",
        "for epoch in range(epochs):\n",
        "  print(f'Epochs: {epoch + 1}')\n",
        "  train_loss = 0\n",
        "  valid_loss = 0\n",
        "\n",
        "  ngram_vectorizer = CountVectorizer(binary=True, ngram_range=(1, 3))\n",
        "  ngram_vectorizer.fit(tweets_list)\n",
        "  X = ngram_vectorizer.transform(tweets_list)\n",
        "  y = tweets_df[\"label\"].values\n",
        "  X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.2, random_state = 0)\n",
        "  svm = LinearSVC()\n",
        "  svm.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apsbUdsqccl5",
        "outputId": "205a0f86-282a-4d8a-912b-bb8ed2e1817e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 1\n",
            "Epochs: 2\n",
            "Epochs: 3\n",
            "Epochs: 4\n",
            "Epochs: 5\n",
            "Epochs: 6\n",
            "Epochs: 7\n",
            "Epochs: 8\n",
            "Epochs: 9\n",
            "Epochs: 10\n",
            "Epochs: 11\n",
            "Epochs: 12\n",
            "Epochs: 13\n",
            "Epochs: 14\n",
            "Epochs: 15\n",
            "Epochs: 16\n",
            "Epochs: 17\n",
            "Epochs: 18\n",
            "Epochs: 19\n",
            "Epochs: 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#clf = LinearSVC()\n",
        "#clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "KVnEWYhVbeUx"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = svm.predict(X_val)\n",
        "print(\"Accuracy: \",accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02lSKNfefs2U",
        "outputId": "5bcfcd76-92dd-4eb7-e9af-6824667521f4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.7515\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.35      0.50       541\n",
            "           1       0.65      0.95      0.77      1485\n",
            "           2       0.88      0.71      0.79      1974\n",
            "\n",
            "    accuracy                           0.75      4000\n",
            "   macro avg       0.79      0.67      0.68      4000\n",
            "weighted avg       0.79      0.75      0.74      4000\n",
            "\n",
            "[[ 191  225  125]\n",
            " [   5 1407   73]\n",
            " [  31  535 1408]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#TF-IDF Vectroization"
      ],
      "metadata": {
        "id": "k9H1j98rdGfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(epochs):\n",
        "  print(f'Epochs: {epoch + 1}')\n",
        "  train_loss = 0\n",
        "  valid_loss = 0\n",
        "\n",
        "  tfidf_vectorizer = TfidfVectorizer()\n",
        "  tfidf_vectorizer.fit(tweets_list)\n",
        "  X = tfidf_vectorizer.transform(tweets_list)\n",
        "  y = tweets_df[\"label\"].values\n",
        "\n",
        "  X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "dht_btO6c-Uq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4902000d-dd35-4756-8100-283393f0293a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 1\n",
            "Epochs: 2\n",
            "Epochs: 3\n",
            "Epochs: 4\n",
            "Epochs: 5\n",
            "Epochs: 6\n",
            "Epochs: 7\n",
            "Epochs: 8\n",
            "Epochs: 9\n",
            "Epochs: 10\n",
            "Epochs: 11\n",
            "Epochs: 12\n",
            "Epochs: 13\n",
            "Epochs: 14\n",
            "Epochs: 15\n",
            "Epochs: 16\n",
            "Epochs: 17\n",
            "Epochs: 18\n",
            "Epochs: 19\n",
            "Epochs: 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm = LinearSVC()\n",
        "svm.fit(X_train, y_train)\n",
        "pred = svm.predict(X_val)\n",
        "print(\"Accuracy: \",accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isRB_LUcdMzt",
        "outputId": "fd5980bc-5b8a-4407-fcd8-5ea5c80bc4ae"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.75475\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.37      0.51       541\n",
            "           1       0.71      0.83      0.77      1485\n",
            "           2       0.78      0.80      0.79      1974\n",
            "\n",
            "    accuracy                           0.75      4000\n",
            "   macro avg       0.78      0.67      0.69      4000\n",
            "weighted avg       0.76      0.75      0.75      4000\n",
            "\n",
            "[[ 201  144  196]\n",
            " [   9 1229  247]\n",
            " [  30  355 1589]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Classification using Logistic Regression"
      ],
      "metadata": {
        "id": "1pTNWAjoMdfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "KJTfm-yRMiGI"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTntorGGMoST",
        "outputId": "455b6e85-95be-4aff-a42d-3dd6dbef4dbb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = lr.predict(X_val)\n",
        "print(\"Accuracy: \",accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuQyqStRMz8k",
        "outputId": "279ca78c-596a-4004-b71f-b6220e040d81"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.73125\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.27      0.41       541\n",
            "           1       0.72      0.77      0.74      1485\n",
            "           2       0.73      0.83      0.78      1974\n",
            "\n",
            "    accuracy                           0.73      4000\n",
            "   macro avg       0.76      0.62      0.64      4000\n",
            "weighted avg       0.74      0.73      0.72      4000\n",
            "\n",
            "[[ 148  137  256]\n",
            " [   4 1142  339]\n",
            " [  26  313 1635]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using TF-IDF Vectorization"
      ],
      "metadata": {
        "id": "6Cm4bHvLNBGg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "tfidf_vectorizer.fit(tweets_list)\n",
        "X = tfidf_vectorizer.transform(tweets_list)\n",
        "y = tweets_df[\"label\"].values\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "IksYELo5M9e7"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQwiFfQ4NJNX",
        "outputId": "01d61618-646f-4a05-878a-81fb7fdfa0b1"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = lr.predict(X_val)\n",
        "print(\"Accuracy:\",accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esYzHciCNM7x",
        "outputId": "a7a9c32e-1abe-4eb4-bb8c-bf299980ac47"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.73125\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.27      0.41       541\n",
            "           1       0.72      0.77      0.74      1485\n",
            "           2       0.73      0.83      0.78      1974\n",
            "\n",
            "    accuracy                           0.73      4000\n",
            "   macro avg       0.76      0.62      0.64      4000\n",
            "weighted avg       0.74      0.73      0.72      4000\n",
            "\n",
            "[[ 148  137  256]\n",
            " [   4 1142  339]\n",
            " [  26  313 1635]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Classification using Naives Bayes"
      ],
      "metadata": {
        "id": "zCuPHlPbNhKg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "MNB = MultinomialNB()\n",
        "MNB.fit(X_train, y_train)\n",
        "pred = MNB.predict(X_val)\n",
        "print(accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbBcueTNNfF3",
        "outputId": "d723096b-cf3c-4ff9-cf7e-611cdc6cca40"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.64075\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.08      0.15       541\n",
            "           1       0.73      0.51      0.60      1485\n",
            "           2       0.60      0.90      0.72      1974\n",
            "\n",
            "    accuracy                           0.64      4000\n",
            "   macro avg       0.77      0.49      0.49      4000\n",
            "weighted avg       0.70      0.64      0.60      4000\n",
            "\n",
            "[[  43   76  422]\n",
            " [   0  751  734]\n",
            " [   1  204 1769]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TF-IDF Vectorization"
      ],
      "metadata": {
        "id": "svxhHiAANr9B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "tfidf_vectorizer.fit(tweets_list)\n",
        "X = tfidf_vectorizer.transform(tweets_list)\n",
        "y = tweets_df[\"label\"].values\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.2, random_state = 0)\n",
        "MNB = MultinomialNB()\n",
        "MNB.fit(X_train, y_train)\n",
        "pred = MNB.predict(X_val)\n",
        "print(\"Accuracy: \",accuracy_score(y_val, pred))\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ejq2HlKBNrmk",
        "outputId": "a87eea15-fef4-4a14-f964-6de4fee672a1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.64075\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.08      0.15       541\n",
            "           1       0.73      0.51      0.60      1485\n",
            "           2       0.60      0.90      0.72      1974\n",
            "\n",
            "    accuracy                           0.64      4000\n",
            "   macro avg       0.77      0.49      0.49      4000\n",
            "weighted avg       0.70      0.64      0.60      4000\n",
            "\n",
            "[[  43   76  422]\n",
            " [   0  751  734]\n",
            " [   1  204 1769]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4L2pgNO7QM_f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}