Real-Time Sentiment Analysis of Social Media Comments Using a Machine Learning
Authors Nandini Yadav V.K Krishna Menon College, Bhandup (East)
Guide: Hiral Joshi Assistant Professor, V.K.K. Menon College, Bhandup (East)
Abstract :
In today’s digital era, social media platforms such as YouTube have become a major source of usergenerated content, providing valuable insights into public opinion. Sentiment analysis of comments enables the measurement of audience emotions and reactions in real-time, which can be beneficial for content creators, marketers, and researchers. In this work, we present a Chrome Extension integrated with a Flask-based backend that analyzes YouTube video comments in real-time. The system fetches comments using the YouTube Data API, processes them through a sentiment analysis model, and visualizes the results directly on the YouTube interface as well as in the extension popup. The proposed solution provides percentage distribution of positive, negative, and neutral comments, enabling a quick and effective understanding of audience perception. Experimental results show that the extension successfully fetches and analyzes comments in real-time with sentiment results displayed to the end user in an interactive format. The work highlights the potential of combining natural language processing (NLP) with browser extensions for real-world applications in social media analytics.
Keywords : Sentiment Analysis, YouTube API, Chrome Extension, Flask, Natural Language Processing, Social Media Analytics
