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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "authorship_tag": "ABX9TyOMZ2fc38YSiBcGv5ssVNYy",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
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+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Add Some more charts for better Understing"
+ ],
+ "metadata": {
+ "id": "4u725hZCGSmA"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# Assume we have a DataFrame 'df' with columns: 'movie', 'joy', 'sadness', 'anger', 'fear', 'surprise'\n",
+ "# This would be the result of your text analysis\n",
+ "\n",
+ "# Sample data (replace this with your actual data)\n",
+ "data = {\n",
+ " 'movie': ['Movie1', 'Movie2', 'Movie3', 'Movie4', 'Movie5'],\n",
+ " 'joy': [0.6, 0.3, 0.8, 0.4, 0.7],\n",
+ " 'sadness': [0.2, 0.5, 0.1, 0.3, 0.2],\n",
+ " 'anger': [0.1, 0.1, 0.05, 0.2, 0.05],\n",
+ " 'fear': [0.05, 0.05, 0.02, 0.05, 0.02],\n",
+ " 'surprise': [0.05, 0.05, 0.03, 0.05, 0.03]\n",
+ "}\n",
+ "\n",
+ "df = pd.DataFrame(data)\n",
+ "\n",
+ "# 1. Stacked Bar Chart of Emotions per Movie\n",
+ "df.set_index('movie').plot(kind='bar', stacked=True, figsize=(10, 6))\n",
+ "plt.title('Emotion Distribution per Movie')\n",
+ "plt.xlabel('Movies')\n",
+ "plt.ylabel('Emotion Intensity')\n",
+ "plt.legend(title='Emotions', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 607
+ },
+ "id": "UClTte9JFFIG",
+ "outputId": "153d2000-4844-416f-9cc1-9351b6769240"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "