praveenraj2206/youtube-sentimental-analysis

The YouTube Comment Sentiment Analyzer is a tool that pulls comments from any YouTube video, figures out whether each comment is positive, negative, or neutral, and then displays those findings on an interactive dashboard. It's designed to give you a clear picture of how an audience is reacting to a video without you having to read thousands of comments yourself. You give it a YouTube video link, and it fetches up to 1,000 comments using the official YouTube API. Then it runs the text of each comment through a sentiment tool called VADER, which is tuned for social media language and assigns a score from negative to positive. It also tracks things like how many likes each comment got, the time of day it was posted, and whether it contains emojis. The results land on a dashboard where you can see pie charts of the sentiment breakdown, trends over time, and a heatmap showing whether positive or negative comments get more engagement. You can also export everything to a spreadsheet. A content creator could use this to check how a recent video landed with their audience. A brand manager could monitor sentiment around a product launch video. A researcher studying online behavior could pull data across multiple videos to analyze patterns in how people respond to different types of content. The tool can also run continuously, fetching new comments every few minutes for ongoing monitoring. The project is built using PySpark for processing, which lets it handle large volumes of comments efficiently. It relies on VADER and TextBlob for sentiment scoring, both established approaches that work well for short social media text. One tradeoff is that the sentiment analysis is English-only for now, and the README notes that multi-language support and more advanced machine learning models are planned for future versions. You'll need a free YouTube API key from Google, plus Java installed on your computer to run PySpark. The README walks through the setup steps clearly.