🚀 Introducing My YouTube Comment Sentiment Analyzer!

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:rocket: Introducing My YouTube Comment Sentiment Analyzer! :dart:

Hey Streamlit Community! :wave: I’m excited to share my latest Streamlit-powered web app that performs real-time sentiment analysis and spam detection on YouTube comments. :bar_chart::sparkles:

:small_blue_diamond: Key Features:
:white_check_mark: Analyze up to 20,000 comments per video using YouTube API
:white_check_mark: Sentiment Classification: Detect Positive, Negative & Neutral comments
:white_check_mark: Spam Detection: Built with a Voting Classifier (Logistic Regression, Random Forest, Naïve Bayes)
:white_check_mark: Interactive Visualizations: Word clouds, bar charts & more!
:white_check_mark: Fast & Optimized: Efficient API handling and progress tracking

:link: Try the App: https://youtube-comment-sentiment-analysis-3rfvhjvsmup6n2im8nzerd.streamlit.app/

Would love to hear your feedback! :rocket::fire: streamlit #MachineLearning #YouTubeAnalytics

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Great job on the app, @Risaal!
I really liked the trending videos section, the word cloud visualizations, and the subtle animations—they make the experience smooth and engaging. One suggestion would be to add chat history and session management to enhance usability further. Keep up the awesome work!

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Thank you for your kind words. Can you please elaborate more on your suggestions

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you can implement session management for each user using st.session_state and maintain context learning via storing the responses to enhance the user experience. So that, if the user wants to get some data for a particular session he can.

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are you planning to open-source it or is this a commercial product? Btw the app isn’t working right now.

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It is open source. And thank you for your reply. I had open my app link and the app is working fine.