• Amaan Sadique Shaikh
• Bhushan Vilas Patil
• Tejas Prashant Sonawane

Music is an integral part of our lives. However, since the social media platforms like TikTok and Instagram have a huge influence on the music charts worldwide, users are exposed solely to mainstream music, therefore the recommendations on music streaming platforms are not very personalized. An song and emotion-based recommendation system permits the users to listen to music based on their emotions. Existing systems use audio signals using the CNN approach and collaborative filtering to recommend songs based on the user's history. The proposed research work develops a personalized system, where the user's current emotion is analyzed with the help of the chat-bot. The chat-bot identifies the user's sentiment by asking some general questions. Based on the input provided by the user, current emotion or mood is analyzed by the chat-bot and it will generate the playlist. The proposed recommendation system utilizes the APIs for the playlist generation and recommendation.

Interactive Chatbot, Application Program Interface, Interactive Systems, Recommender Systems, Playlist generation, IBM Tone Analyzer API, Last.fm API, CakeChat server.

"Chatbot Song Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.d222-d226, December-2021, Available :http://www.jetir.org/papers/JETIR2112328.pdf

"Chatbot Song Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppd222-d226, December-2021, Available at : http://www.jetir.org/papers/JETIR2112328.pdf