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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentiment Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"\n",
"import re\n",
"from functools import cache\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data: pd.DataFrame = None # TODO: load dataset\n",
"stopwords: set[str] = None # TODO: load stopwords"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the distribution\n",
"_, ax = plt.subplots(figsize=(6, 4))\n",
"data[\"sentiment\"].value_counts().plot(kind=\"bar\", ax=ax)\n",
"ax.set_xticklabels([\"Negative\", \"Positive\"], rotation=0)\n",
"ax.set_xlabel(\"Sentiment\")\n",
"ax.grid(False)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@cache\n",
"def extract_words(text: str) -> list[str]:\n",
" return re.findall(r\"(\\b[^\\s]+\\b)\", text.lower())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extract words and count them\n",
"words = data[\"text\"].apply(extract_words).explode()\n",
"word_counts = words.value_counts().reset_index()\n",
"word_counts.columns = [\"word\", \"count\"]\n",
"word_counts.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the most common words\n",
"_, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
"\n",
"sns.barplot(data=word_counts.head(10), x=\"count\", y=\"word\", ax=ax1)\n",
"ax1.set_title(\"Most common words\")\n",
"ax1.grid(False)\n",
"ax1.tick_params(axis=\"x\", rotation=45)\n",
"\n",
"ax2.set_title(\"Most common words (excluding stopwords)\")\n",
"sns.barplot(\n",
" data=word_counts[~word_counts[\"word\"].isin(stopwords)].head(10),\n",
" x=\"count\",\n",
" y=\"word\",\n",
" ax=ax2,\n",
")\n",
"ax2.grid(False)\n",
"ax2.tick_params(axis=\"x\", rotation=45)\n",
"ax2.set_ylabel(\"\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find best classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find best hyperparameters"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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