• Akhib Khan
• N.Koteswara Rao G.Pallavi

The exponential growth of user-generated content on platforms like YouTube necessitates advanced techniques for sentiment analysis to understand public opinion. This study presents a comprehensive approach to sentiment analysis of YouTube comments using data science techniques, specifically employing Naive Bayes and Support Vector Machine (SVM) algorithms. By leveraging the YouTube API, we systematically extract and preprocess comment data, followed by feature extraction using techniques such as TF-IDF. The performance of the Naive Bayes and SVM classifiers is evaluated and compared to determine their efficacy in accurately categorizing sentiments as positive, negative, or neutral. The implementation is encapsulated in an interactive Streamlit application, providing an accessible interface for real-time sentiment analysis. This project not only demonstrates the practical application of machine learning algorithms in natural language processing but also offers insights into public sentiment dynamics on social media platforms.

Sentiment Analysis, YouTube Comments, Naive Bayes, Support Vector Machine, YouTube API, Streamlit, Machine Learning, Natural Language Processing, Data Science.

"SENTIMENT ANALYSIS OF YOUTUBE COMMENTS USING DATA SCIENCE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.583-586, June-2024, Available :http://www.jetir.org/papers/JETIRGL06097.pdf

"SENTIMENT ANALYSIS OF YOUTUBE COMMENTS USING DATA SCIENCE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp583-586, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06097.pdf