YouTube Comment Analysis Using LSTM Model
D. Pavithra, P Poovizhi, G. Rokeshkumar, T. Bharathvaj, M. Mageshkumar · 2025
Addressing the ever-expanding landscape of online content consumption, this study introduces a groundbreaking approach to real-time sentiment analysis and comment categorization on platforms such as YouTube. Given the remarkable rate of video uploads, with a new video posted every minute globally, and an impressive daily consumption of content reaching one billion hours, the manual analysis of user-generated content has become impractical. The proposed system leverages long short-term memory (LSTM) recurrent neural networks (RNNs), specifically a stack of three LSTM layers, for effective sentiment analysis, overcoming challenges like vanishing and exploding gradients. In addition to sentiment analysis, the system employs an incremental updation approach for swift comment analysis and storage in the database, ensuring real-time analytics. Taking a step beyond traditional sentiment analysis, the system introduces a novel feature – comment categorization. Comments are not only analyzed for sentiment but are also categorized and stored, providing content creators with a segregated view of user feedback. This innovative approach enables content creators to enhance their videos based on specific comment categories, fostering an environment for improved content creation and audience engagement. In summary, this system represents a significant stride towards revolutionizing online content analysis, offering real-time insights and empowering content creators for a more dynamic and interactive content creation process.