🎯 Sentiment Analysis on YouTube Comments

This project performs sentiment analysis on YouTube comments using Natural Language Processing (NLP) and machine learning. It classifies comments into Positive, Negative, or Neutral categories and provides visual insights using word clouds and charts.

---

📁 Dataset

The dataset consists of YouTube video comments along with manually labeled sentiments.
Format:

• Comment: Text of the comment
• Sentiment: One of positive, negative, or neutral

🔄 You can use web scraping or YouTube APIs to extract your own dataset.

---

🔍 Project Workflow

1. Data Cleaning & Preprocessing
   • Remove stopwords, punctuation
   • Tokenization using nltk
   • Lowercasing and lemmatization
2. Exploratory Data Analysis (EDA)
   • Sentiment distribution bar chart
   • Word clouds for positive & negative comments
   • Topic modeling using LDA
3. Feature Extraction
   • TF-IDF Vectorizer
4. Model Building
   • Logistic Regression (can be replaced with XGBoost/SVM)
   • Evaluation: Accuracy, Precision, Recall, F1-score
5. Deployment (Optional)
   • Flask app to predict sentiment of user-input comments

---

🧠 Libraries Used

pandas
numpy
nltk
matplotlib
seaborn
sklearn
wordcloud
flask (optional for deployment)