• Prof. Shreedhar Yadawad
• Sudhanva R M
• Naveen S H
• Prateek D
• Manojkumar J

In today's digital landscape, YouTube has become a vital hub for diverse content, from educational tutorials to entertainment. However, understanding viewer sentiment and engagement is a challenge for creators and marketers. To tackle this, we introduce a YouTube sentiment analysis project to extract insights from user comments. With YouTube's audience expanding rapidly, decoding emotional nuances and interaction patterns becomes complex. Our automated system, powered by AI and ML, assesses sentiment, categorizes topics, identifies key entities, and gauges anticipation for sequels. By analyzing comments, it empowers creators and marketers with data-driven decisions, tailoring content to audience preferences. Through sentiment analysis, NLP, and visualization, our project aims to enhance content experiences on YouTube, addressing challenges and fostering strategic content creation in the digital realm.

Sentiment Analysis, Name Entity Recognition, Natural Language Processing (NLP), Podcast

"YouTube Analytics: Deciphering YouTube Comments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o52-o57, April-2024, Available :http://www.jetir.org/papers/JETIR2404F09.pdf

"YouTube Analytics: Deciphering YouTube Comments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo52-o57, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F09.pdf