Sentiment Analysis for YouTube Comment using

The YouTube Comments and Videos Sentiment Analysis project is an advanced system that automates the sentiment classification of both comments and video transcripts associated with YouTube videos. Using Natural Language Processing (NLP) and cutting-edge deep learning models, the system determines whether the sentiments expressed are positive, negative, or neutral. The system integrates tools such as the YouTube Data API to extract comments and the YouTube Transcript API to retrieve video transcripts. Advanced preprocessing techniques like tokenization, lemmatization, and stopword removal are applied to prepare the text for sentiment analysis. The project employs pre-trained models, including BERT for comments sentiment analysis and LSTM and GRU for video transcript sentiment analysis, ensuring high accuracy in sentiment classification. Users interact with the system through a web-based interface where they can input a YouTube video URL. The system then retrieves comments and transcripts, performs sentiment analysis, and visualizes the results in an intuitive graphical format. Metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models, ensuring the robustness of the system. This project provides content creators, marketers, and businesses with real-time insights into public sentiment, enabling them to make informed decisions efficiently. By automating the sentiment analysis process, the system offers a scalable, user-friendly, and reliable solution for understanding audience reactions on YouTube. Keywords: sentiment, YouTube comments, YouTube URL, LSTM, GRU, BERT, NLP.