mini-projet2-finalll.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af1a8c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from apiclient.discovery import build\n",
    "from flask import request\n",
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "from pprint import pprint as pp \n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.dates as mdates\n",
    "import pandas as pd\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "52046c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "API_Key=\"****************\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "666f9262",
   "metadata": {},
   "outputs": [],
   "source": [
    "youtube = build('youtube','v3',developerKey = API_Key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "02cee0e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "HttpError",
     "evalue": "<HttpError 403 when requesting https://youtube.googleapis.com/youtube/v3/channels?part=snippet&id=UCn4-pzhxaUGxFF6T_SQ1Efw&key=AIzaSyBp7C_eEn6aWZyfUmTzY1N3_uFid0-rxaY&alt=json returned \"The request cannot be completed because you have exceeded your <a href=\"/youtube/v3/getting-started#quota\">quota</a>.\". Details: \"[{'message': 'The request cannot be completed because you have exceeded your <a href=\"/youtube/v3/getting-started#quota\">quota</a>.', 'domain': 'youtube.quota', 'reason': 'quotaExceeded'}]\">",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHttpError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[51], line 56\u001b[0m\n\u001b[0;32m     53\u001b[0m video_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUIZAiXYceBI\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;66;03m# Get comments for a single video\u001b[39;00m\n\u001b[1;32m---> 56\u001b[0m video_comments_df \u001b[38;5;241m=\u001b[39m get_video_comments(api_key, video_id)\n\u001b[0;32m     58\u001b[0m \u001b[38;5;66;03m# Save the DataFrame to a CSV file\u001b[39;00m\n\u001b[0;32m     59\u001b[0m csv_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myoutube_single_video_comments.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "Cell \u001b[1;32mIn[51], line 27\u001b[0m, in \u001b[0;36mget_video_comments\u001b[1;34m(api_key, video_id)\u001b[0m\n\u001b[0;32m     21\u001b[0m user_channel_id \u001b[38;5;241m=\u001b[39m item[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnippet\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtopLevelComment\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnippet\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauthorChannelId\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m     23\u001b[0m \u001b[38;5;66;03m# Try to get user's channel information to extract region\u001b[39;00m\n\u001b[0;32m     24\u001b[0m user_info \u001b[38;5;241m=\u001b[39m youtube\u001b[38;5;241m.\u001b[39mchannels()\u001b[38;5;241m.\u001b[39mlist(\n\u001b[0;32m     25\u001b[0m     part\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnippet\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28mid\u001b[39m\u001b[38;5;241m=\u001b[39muser_channel_id\n\u001b[1;32m---> 27\u001b[0m )\u001b[38;5;241m.\u001b[39mexecute()\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m user_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mitems\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m     30\u001b[0m     comment_region \u001b[38;5;241m=\u001b[39m user_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mitems\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnippet\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcountry\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\googleapiclient\\_helpers.py:130\u001b[0m, in \u001b[0;36mpositional.<locals>.positional_decorator.<locals>.positional_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m positional_parameters_enforcement \u001b[38;5;241m==\u001b[39m POSITIONAL_WARNING:\n\u001b[0;32m    129\u001b[0m         logger\u001b[38;5;241m.\u001b[39mwarning(message)\n\u001b[1;32m--> 130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\googleapiclient\\http.py:938\u001b[0m, in \u001b[0;36mHttpRequest.execute\u001b[1;34m(self, http, num_retries)\u001b[0m\n\u001b[0;32m    936\u001b[0m     callback(resp)\n\u001b[0;32m    937\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[1;32m--> 938\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HttpError(resp, content, uri\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muri)\n\u001b[0;32m    939\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpostproc(resp, content)\n",
      "\u001b[1;31mHttpError\u001b[0m: <HttpError 403 when requesting https://youtube.googleapis.com/youtube/v3/channels?part=snippet&id=UCn4-pzhxaUGxFF6T_SQ1Efw&key=AIzaSyBp7C_eEn6aWZyfUmTzY1N3_uFid0-rxaY&alt=json returned \"The request cannot be completed because you have exceeded your <a href=\"/youtube/v3/getting-started#quota\">quota</a>.\". Details: \"[{'message': 'The request cannot be completed because you have exceeded your <a href=\"/youtube/v3/getting-started#quota\">quota</a>.', 'domain': 'youtube.quota', 'reason': 'quotaExceeded'}]\">"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import googleapiclient.discovery\n",
    "\n",
    "def get_video_comments(api_key, video_id):\n",
    "    youtube = googleapiclient.discovery.build(\"youtube\", \"v3\", developerKey=api_key)\n",
    "\n",
    "    comments = []\n",
    "    nextPageToken = None\n",
    "\n",
    "    while True:\n",
    "        response = youtube.commentThreads().list(\n",
    "            part=\"snippet\",\n",
    "            videoId=video_id,\n",
    "            maxResults=100,  # You can adjust this value based on your needs\n",
    "            pageToken=nextPageToken\n",
    "        ).execute()\n",
    "\n",
    "        for item in response[\"items\"]:\n",
    "            comment_text = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"textDisplay\"]\n",
    "            comment_date = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"publishedAt\"]\n",
    "            user_channel_id = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"authorChannelId\"][\"value\"]\n",
    "\n",
    "            # Try to get user's channel information to extract region\n",
    "            user_info = youtube.channels().list(\n",
    "                part=\"snippet\",\n",
    "                id=user_channel_id\n",
    "            ).execute()\n",
    "\n",
    "            if user_info.get(\"items\"):\n",
    "                comment_region = user_info[\"items\"][0][\"snippet\"].get(\"country\", \"Unknown\")\n",
    "            else:\n",
    "                comment_region = \"Unknown\"\n",
    "\n",
    "            # Add the comment to the list along with additional information\n",
    "            comments.append({\n",
    "                \"VideoId\": video_id,\n",
    "                \"Comments\": comment_text,\n",
    "                \"Date\": comment_date,\n",
    "                \"Region\": comment_region\n",
    "            })\n",
    "\n",
    "        nextPageToken = response.get(\"nextPageToken\")\n",
    "\n",
    "        if not nextPageToken:\n",
    "            break\n",
    "\n",
    "    return pd.DataFrame(comments)\n",
    "\n",
    "# Replace YOUR_API_KEY with your actual API key\n",
    "api_key = \"AIzaSyBp7C_eEn6aWZyfUmTzY1N3_uFid0-rxaY\"\n",
    "\n",
    "# Replace VIDEO_ID with the video ID you want to retrieve comments from\n",
    "video_id = \"UIZAiXYceBI\"\n",
    "\n",
    "# Get comments for a single video\n",
    "video_comments_df = get_video_comments(api_key, video_id)\n",
    "\n",
    "# Save the DataFrame to a CSV file\n",
    "csv_filename = \"youtube_single_video_comments.csv\"\n",
    "video_comments_df.to_csv(csv_filename, index=False)\n",
    "\n",
    "# Print the first 10 rows of the DataFrame and the CSV filename as an example\n",
    "print(video_comments_df.head(10))\n",
    "print(f\"\\nDataFrame saved to {csv_filename}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d5ee25c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned data has been saved to youtube_data_cleaned.csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Download NLTK resources\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "def preprocess_text(text):\n",
    "    # Convert to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Remove links\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    \n",
    "    # Remove HTML tags\n",
    "    text = re.sub(r'<.*?>', '', text)\n",
    "    \n",
    "    # Remove numbers\n",
    "    text = re.sub(r'\\d+', '', text)\n",
    "    \n",
    "    # Remove punctuation\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    \n",
    "    # Tokenization\n",
    "    tokens = word_tokenize(text)\n",
    "    \n",
    "    # Remove stopwords\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "    \n",
    "    # Stemming\n",
    "    stemmer = PorterStemmer()\n",
    "    tokens = [stemmer.stem(word) for word in tokens]\n",
    "    \n",
    "    # Lemmatization\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
    "    \n",
    "    # Join the tokens back into a single string\n",
    "    processed_text = ' '.join(tokens)\n",
    "    \n",
    "    return processed_text\n",
    "\n",
    "# Read the CSV file\n",
    "csv_file = 'youtube_single_video_comments.csv'\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# Apply preprocessing to the 'Comment' column\n",
    "df['ProcessedComment'] = df['Comments'].apply(preprocess_text)\n",
    "\n",
    "# Save the cleaned data to a new CSV file\n",
    "cleaned_csv_file = 'youtube_data_cleaned.csv'\n",
    "df.to_csv(cleaned_csv_file, index=False)\n",
    "\n",
    "print(f'Cleaned data has been saved to {cleaned_csv_file}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "202cb382",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataComment = pd.read_csv(r'youtube_data_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecb87146",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Keywords:\n",
      "googl: 456\n",
      "ai: 374\n",
      "video: 322\n",
      "gemini: 313\n",
      "like: 281\n",
      "fake: 235\n",
      "impress: 200\n",
      "see: 168\n",
      "use: 161\n",
      "real: 159\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "641babc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered data has been saved to youtube_data_filtered.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read the cleaned data CSV file\n",
    "cleaned_csv_file = 'youtube_data_cleaned.csv'\n",
    "df = pd.read_csv(cleaned_csv_file)\n",
    "\n",
    "# Fill NaN values in the 'ProcessedComment' column with an empty string\n",
    "df['ProcessedComment'] = df['ProcessedComment'].fillna('')\n",
    "\n",
    "# Define the keywords you want to filter by\n",
    "keywords = ['googl', 'ai', 'gemini', 'video', 'like', 'fake', 'reel', 'use', 'see', 'impress']\n",
    "\n",
    "# Filter the data based on keywords\n",
    "filtered_df = df[df['ProcessedComment'].str.contains('|'.join(keywords))]\n",
    "\n",
    "# Save the filtered data to a new CSV file\n",
    "filtered_csv_file = 'youtube_data_filtered.csv'\n",
    "filtered_df.to_csv(filtered_csv_file, index=False)\n",
    "\n",
    "print(f'Filtered data has been saved to {filtered_csv_file}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763e0618",
   "metadata": {},
   "source": [
    "-------------------------------------------- with vader ----------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1b514ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Labels:\n",
      "                                       ProcessedComment SentimentLabel\n",
      "0            yet android ai assist cant set simpl timer        neutral\n",
      "1                                                  fake       negative\n",
      "2     realtim shot one take impress setup creat tool...       positive\n",
      "3                                                woohoo       positive\n",
      "4     imagin potenti thing human ai pin one first wi...        neutral\n",
      "...                                                 ...            ...\n",
      "2819                                            ai suck       negative\n",
      "2820                                         agi happen        neutral\n",
      "2821                                              third        neutral\n",
      "2822                          alway love ai video googl       positive\n",
      "2823                                              first        neutral\n",
      "\n",
      "[2824 rows x 2 columns]\n",
      "Data with sentiment labels has been saved to youtube_data_sentiment.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# Download NLTK resources if not already downloaded\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "# Read the cleaned data CSV file\n",
    "cleaned_csv_file = 'youtube_data_cleaned.csv'\n",
    "df = pd.read_csv(cleaned_csv_file)\n",
    "\n",
    "# Fill NaN values in the 'ProcessedComment' column with an empty string\n",
    "df['ProcessedComment'] = df['ProcessedComment'].fillna('')\n",
    "\n",
    "# Initialize the VADER sentiment analyzer\n",
    "sia = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Define a function to get sentiment polarity labels\n",
    "def get_sentiment_label(score):\n",
    "    if score >= 0.05:\n",
    "        return 'positive'\n",
    "    elif score <= -0.05:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'neutral'\n",
    "\n",
    "# Analyze the sentiment of each comment\n",
    "df['SentimentScore'] = df['ProcessedComment'].apply(lambda x: sia.polarity_scores(x)['compound'])\n",
    "df['SentimentLabel'] = df['SentimentScore'].apply(get_sentiment_label)\n",
    "\n",
    "# Display the sentiment labels\n",
    "print(\"Sentiment Labels:\")\n",
    "print(df[['ProcessedComment', 'SentimentLabel']])\n",
    "\n",
    "# Save the data with sentiment labels to a new CSV file\n",
    "sentiment_csv_file = 'youtube_data_sentiment.csv'\n",
    "df.to_csv(sentiment_csv_file, index=False)\n",
    "\n",
    "print(f'Data with sentiment labels has been saved to {sentiment_csv_file}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a43838b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the data with sentiment labels\n",
    "sentiment_csv_file = 'youtube_data_sentiment.csv'\n",
    "df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive, negative, and neutral comments\n",
    "sentiment_counts = df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sentiment_counts.plot(kind='bar', color=['green', 'red', 'gray'])\n",
    "plt.title('Distribution of Sentiments')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Number of Comments')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "42f08a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the data with sentiment labels (if not already read)\n",
    "sentiment_csv_file = 'youtube_data_sentiment.csv'\n",
    "df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive, negative, and neutral comments\n",
    "sentiment_counts = df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a pie chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['green', 'red', 'gray']\n",
    "plt.pie(sentiment_counts, labels=sentiment_counts.index, colors=colors, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Sentiments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d407c67",
   "metadata": {},
   "source": [
    "----------------------------------------------with textBlob---------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f3d6cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Labels:\n",
      "                                       ProcessedComment SentimentLabel\n",
      "0            yet android ai assist cant set simpl timer        neutral\n",
      "1                                                  fake       negative\n",
      "2     realtim shot one take impress setup creat tool...        neutral\n",
      "3                                                woohoo        neutral\n",
      "4     imagin potenti thing human ai pin one first wi...        neutral\n",
      "...                                                 ...            ...\n",
      "2819                                            ai suck        neutral\n",
      "2820                                         agi happen        neutral\n",
      "2821                                              third        neutral\n",
      "2822                          alway love ai video googl       positive\n",
      "2823                                              first       positive\n",
      "\n",
      "[2824 rows x 2 columns]\n",
      "Data with sentiment labels using TextBlob has been saved to youtube_data_sentiment_textblob.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from textblob import TextBlob\n",
    "\n",
    "# Read the cleaned data CSV file\n",
    "cleaned_csv_file = 'youtube_data_cleaned.csv'\n",
    "df = pd.read_csv(cleaned_csv_file)\n",
    "\n",
    "# Fill NaN values in the 'ProcessedComment' column with an empty string\n",
    "df['ProcessedComment'] = df['ProcessedComment'].fillna('')\n",
    "\n",
    "# Define a function to get sentiment polarity labels using TextBlob\n",
    "def get_sentiment_label(text):\n",
    "    analysis = TextBlob(text)\n",
    "    # Use a threshold to classify sentiments\n",
    "    if analysis.sentiment.polarity > 0.05:\n",
    "        return 'positive'\n",
    "    elif analysis.sentiment.polarity < -0.05:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'neutral'\n",
    "\n",
    "# Analyze the sentiment of each comment\n",
    "df['SentimentLabel'] = df['ProcessedComment'].apply(get_sentiment_label)\n",
    "\n",
    "# Display the sentiment labels\n",
    "print(\"Sentiment Labels:\")\n",
    "print(df[['ProcessedComment', 'SentimentLabel']])\n",
    "\n",
    "# Save the data with sentiment labels to a new CSV file\n",
    "sentiment_csv_file = 'youtube_data_sentiment_textblob.csv'\n",
    "df.to_csv(sentiment_csv_file, index=False)\n",
    "\n",
    "print(f'Data with sentiment labels using TextBlob has been saved to {sentiment_csv_file}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92719a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the data with sentiment labels\n",
    "sentiment_csv_file = 'youtube_data_sentiment_textblob.csv'\n",
    "df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive and negative comments\n",
    "sentiment_counts = df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sentiment_counts.plot(kind='bar', color=['green', 'red'])\n",
    "plt.title('Distribution of Positive and Negative Comments')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Number of Comments')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f58a0b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the data with sentiment labels\n",
    "sentiment_csv_file = 'youtube_data_sentiment_textblob.csv'\n",
    "df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive and negative comments\n",
    "sentiment_counts = df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a pie chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['green', 'red','yellow']\n",
    "plt.pie(sentiment_counts, labels=sentiment_counts.index, colors=colors, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Positive and Negative Comments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84954f27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['VideoId', 'Comments', 'Date', 'Region'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(video_comments_df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9f4eadbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the data with sentiment labels\n",
    "sentiment_csv_file = 'youtube_data_sentiment_textblob.csv'\n",
    "df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive and negative comments\n",
    "sentiment_counts = df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a bar chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Bar chart\n",
    "plt.subplot(1, 2, 1)\n",
    "sentiment_counts.plot(kind='bar', color=['green', 'red','yellow'])\n",
    "plt.title('Distribution of Positive and Negative Comments (Bar Chart)')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Number of Comments')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Pie chart\n",
    "plt.subplot(1, 2, 2)\n",
    "colors = ['green', 'red','yellow']\n",
    "plt.pie(sentiment_counts, labels=sentiment_counts.index, colors=colors, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Positive and Negative Comments (Pie Chart)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7c8dc6",
   "metadata": {},
   "source": [
    "---------------------------------------with vadr +and positifs and  negatif dataset ------------------------------\n",
    "i've used 2 folders downloaded from kaggle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b51fb32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\meriem\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Labels:\n",
      "                                       ProcessedComment SentimentLabel\n",
      "1                                                  fake       negative\n",
      "2     realtim shot one take impress setup creat tool...       positive\n",
      "3                                                woohoo       positive\n",
      "5     oh bank card number seem number oopsoh wife se...       positive\n",
      "6                                                   wow       positive\n",
      "...                                                 ...            ...\n",
      "2814                           continu amaz thank googl       positive\n",
      "2815  super cool experi googl home googl assist wors...       positive\n",
      "2818         feel like wouldnt take long tell bard stfu       positive\n",
      "2819                                            ai suck       negative\n",
      "2822                          alway love ai video googl       positive\n",
      "\n",
      "[1388 rows x 2 columns]\n",
      "Data with sentiment labels for positive and negative words (excluding neutral) has been saved to youtube_data_sentiment_filtered.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# Download NLTK resources if not already downloaded\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "# Read the cleaned data CSV file\n",
    "cleaned_csv_file = 'youtube_data_cleaned.csv'\n",
    "df = pd.read_csv(cleaned_csv_file)\n",
    "\n",
    "# Fill NaN values in the 'ProcessedComment' column with an empty string\n",
    "df['ProcessedComment'] = df['ProcessedComment'].fillna('')\n",
    "\n",
    "# Define a function to get sentiment polarity labels\n",
    "def get_sentiment_label(score):\n",
    "    if score >= 0.05:\n",
    "        return 'positive'\n",
    "    elif score <= -0.05:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return None  # Return None for neutral comments\n",
    "\n",
    "# Read positive and negative words from TXT files\n",
    "with open('positive-words.txt', 'r') as file:\n",
    "    positive_words = [line.strip().lower() for line in file]\n",
    "\n",
    "with open('negative-words.txt', 'r') as file:\n",
    "    negative_words = [line.strip().lower() for line in file]\n",
    "\n",
    "# Create a mask for positive and negative comments\n",
    "positive_mask = df['ProcessedComment'].apply(lambda x: any(word in x.lower() for word in positive_words))\n",
    "negative_mask = df['ProcessedComment'].apply(lambda x: any(word in x.lower() for word in negative_words))\n",
    "\n",
    "# Apply sentiment analysis only to the filtered data\n",
    "filtered_df = df[positive_mask | negative_mask].copy()\n",
    "sia = SentimentIntensityAnalyzer()\n",
    "filtered_df['SentimentScore'] = filtered_df['ProcessedComment'].apply(lambda x: sia.polarity_scores(x)['compound'])\n",
    "filtered_df['SentimentLabel'] = filtered_df['SentimentScore'].apply(get_sentiment_label)\n",
    "\n",
    "# Drop rows with neutral sentiment\n",
    "filtered_df = filtered_df.dropna(subset=['SentimentLabel'])\n",
    "\n",
    "# Display the sentiment labels\n",
    "print(\"Sentiment Labels:\")\n",
    "print(filtered_df[['ProcessedComment', 'SentimentLabel']])\n",
    "\n",
    "# Save the filtered data with sentiment labels to a new CSV file\n",
    "sentiment_csv_file = 'youtube_data_sentiment_filtered.csv'\n",
    "filtered_df.to_csv(sentiment_csv_file, index=False)\n",
    "\n",
    "print(f'Data with sentiment labels for positive and negative words (excluding neutral) has been saved to {sentiment_csv_file}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eebceb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the data with sentiment labels\n",
    "sentiment_csv_file = 'youtube_data_sentiment_filtered.csv'\n",
    "filtered_df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive and negative comments\n",
    "sentiment_counts = filtered_df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sentiment_counts.plot(kind='bar', color=['green', 'red'])\n",
    "plt.title('Distribution of Positive and Negative Comments')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Number of Comments')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8d8b0844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentiment_csv_file = 'youtube_data_sentiment_filtered.csv'\n",
    "filtered_df = pd.read_csv(sentiment_csv_file)\n",
    "\n",
    "# Count the number of positive and negative comments\n",
    "sentiment_counts = filtered_df['SentimentLabel'].value_counts()\n",
    "\n",
    "# Plot the distribution as a pie chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['green', 'red']\n",
    "plt.pie(sentiment_counts, labels=sentiment_counts.index, colors=colors, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Positive and Negative Comments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2248ff5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les 5 principaux pays ou régions :\n",
      "Region\n",
      "Unknown    2321\n",
      "US          135\n",
      "IN           64\n",
      "GB           30\n",
      "KR           23\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Lire le fichier CSV avec les commentaires\n",
    "csv_filename = \"youtube_single_video_comments.csv\"\n",
    "video_comments_df = pd.read_csv(csv_filename)\n",
    "\n",
    "# Afficher les 5 principaux pays ou régions\n",
    "top_regions = video_comments_df['Region'].value_counts().head(5)\n",
    "\n",
    "# Afficher les résultats\n",
    "print(\"Les 5 principaux pays ou régions :\")\n",
    "print(top_regions)\n"
   ]
  }
 ],
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