YouTube Video Comments Sentiment Analysis Using Custom NLP Model

Abstract

This paper presents a sentiment analysis platform designed to process user comments on YouTube videos. Leveraging an LSTM-based neural network trained on large-scale datasets such as Sentiment140 and a 3-million-row Twitter sentiment dataset, the platform categorizes comments into positive, negative, or neutral sentiments. It integrates modern web technologies like React.js for the front-end, Flask for the back-end, Firebase for user authentication, and the YouTube Data API for comment retrieval. Additionally, OpenAI's language models are employed to provide advanced contextual analysis, extracting key themes and emotional trends from the comments. The system visualizes sentiment distributions using dynamic charts, offering insights valuable for content creators and researchers. This work demonstrates the effective combination of deep learning and scalable web technologies to build a user-friendly sentiment analysis solution.