Built a comprehensive sentiment analysis system for YouTube comments using Natural Language Processing techniques. The project involved extracting comments via YouTube API and applying advanced NLP preprocessing to analyze sentiment patterns.
Automated extraction of comments from YouTube videos using official YouTube Data API
Advanced text preprocessing including punctuation removal, stopword filtering, and numerical value cleaning
Machine learning models to classify comments as positive, negative, or neutral sentiment
• Data Collection: Used YouTube Data API v3 for comment extraction
• Text Preprocessing: Implemented comprehensive cleaning pipeline with NLTK
• Feature Engineering: Created TF-IDF vectors for text representation
• Model Training: Trained multiple classifiers for sentiment prediction
• Evaluation: Used accuracy, precision, recall metrics for model assessment
• Successfully processed thousands of YouTube comments with high accuracy
• Achieved robust sentiment classification across different video categories
• Developed reusable NLP pipeline for future text analysis projects
• Gained expertise in API integration and large-scale text processing