• Duvvi Rajesh
• Dr G.Sharmila Sujatha

In the age of digital transformation, personalization is a key driver of user satisfaction, particularly in the realm of music streaming. This paper explores the development of a mood-based song recommender system that leverages Natural Language Processing (NLP) and machine learning (ML) algorithms to enhance user experience through personalized music recommendations based on emotional states. The system analyzes user-generated text to predict mood and uses the Spotify API to suggest songs aligned with these moods. Various machine learning models were evaluated, with Random Forest emerging as the top performer, achieving a high accuracy rate in mood classification. The system's ability to recommend mood-appropriate music provides a practical application for emotion-aware technology in enhancing user engagement and satisfaction. Here's the revised introduction with the addition of the chatbot song recommender system, SVM, and Random Forest content

"Chat bot Song Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.e767-e770, August-2024, Available :http://www.jetir.org/papers/JETIR2408590.pdf

"Chat bot Song Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppe767-e770, August-2024, Available at : http://www.jetir.org/papers/JETIR2408590.pdf