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Added Sentimental Analysis Code #13 #53

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May 14, 2024
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Added Sentimental Analysis Code #13 #53

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76 changes: 72 additions & 4 deletions Movie_review_imdb_scrapping.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -22,16 +22,42 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: textblob in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (0.18.0.post0)\n",
"Requirement already satisfied: nltk>=3.8 in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (from textblob) (3.8.1)\n",
"Requirement already satisfied: click in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (from nltk>=3.8->textblob) (8.0.4)\n",
"Requirement already satisfied: joblib in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (from nltk>=3.8->textblob) (1.2.0)\n",
"Requirement already satisfied: regex>=2021.8.3 in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (from nltk>=3.8->textblob) (2022.7.9)\n",
"Requirement already satisfied: tqdm in c:\\users\\umesh\\anaconda3.x\\lib\\site-packages (from nltk>=3.8->textblob) (4.65.0)\n",
"Requirement already satisfied: colorama in c:\\users\\umesh\\appdata\\roaming\\python\\python311\\site-packages (from click->nltk>=3.8->textblob) (0.4.6)\n"
]
},
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'textblob'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[5], line 7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m \n\u001b[0;32m 6\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install textblob\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtextblob\u001b[39;00m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'textblob'"
]
}
],
"source": [
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import itertools #to create efficent looping to fetch more data in a go\n",
"import re \n",
"import random "
"import random \n",
"from textblob import TextBlob"
]
},
{
Expand Down Expand Up @@ -778,6 +804,48 @@
" g.close()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'TextBlob' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[6], line 22\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;66;03m# Example usage:\u001b[39;00m\n\u001b[0;32m 21\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI absolutely loved this movie! It was fantastic.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 22\u001b[0m sentiment \u001b[38;5;241m=\u001b[39m \u001b[43manalyze_sentiment\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSentiment:\u001b[39m\u001b[38;5;124m\"\u001b[39m, sentiment)\n\u001b[0;32m 24\u001b[0m \u001b[38;5;66;03m# Assuming df is your DataFrame containing the reviews\u001b[39;00m\n",
"Cell \u001b[1;32mIn[6], line 10\u001b[0m, in \u001b[0;36manalyze_sentiment\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyze_sentiment\u001b[39m(text):\n\u001b[0;32m 2\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;124;03m Analyzes the sentiment of the input text.\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;124;03m \u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;124;03m - 'neutral' if sentiment polarity == 0\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m blob \u001b[38;5;241m=\u001b[39m \u001b[43mTextBlob\u001b[49m(text)\n\u001b[0;32m 11\u001b[0m polarity \u001b[38;5;241m=\u001b[39m blob\u001b[38;5;241m.\u001b[39msentiment\u001b[38;5;241m.\u001b[39mpolarity\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m polarity \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"\u001b[1;31mNameError\u001b[0m: name 'TextBlob' is not defined"
]
}
],
"source": [
"def analyze_sentiment(text):\n",
" \"\"\"\n",
" Analyzes the sentiment of the input text.\n",
" \n",
" Returns:\n",
" - 'positive' if sentiment polarity > 0\n",
" - 'negative' if sentiment polarity < 0\n",
" - 'neutral' if sentiment polarity == 0\n",
" \"\"\"\n",
" blob = TextBlob(text)\n",
" polarity = blob.sentiment.polarity\n",
" \n",
" if polarity > 0:\n",
" return 'positive'\n",
" elif polarity < 0:\n",
" return 'negative'\n",
" else:\n",
" return 'neutral'\n",
"\n",
"# Assuming df is your DataFrame containing the reviews\n",
"df['sentiment'] = df['user_review'].apply(analyze_sentiment)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -818,7 +886,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.1"
"version": "3.11.4"
},
"orig_nbformat": 4
},
Expand Down