Merge pull request #53 from Shivani0230/main

Added Sentimental Analysis Code #13

Author: sanjay-kv

Movie_review_imdb_scrapping.ipynb

@@ -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"
    ]
   },
   {
@@ -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": {},
@@ -818,7 +886,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.11.4"
   },
   "orig_nbformat": 4
  },