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In This Project, We Present A Novel Web Application That Harnesses The Power Of Natural Language Processing And Deep Learning To Analyze Emotions In Textual Input And Provide Personalized Music Recommendations Based On The Detected Mood. The Main Goal Of This Project Is To Create An Interactive And Engaging Platform That Enhances User Experience By Understanding Their Emotions And Offering Appropriate Music Selections In Real-time. The Core Component Of Our System Is An Emotion-driven Chatbot, Which Utilizes The Long Short-Term Memory (LSTM) Algorithm, A Type Of Recurrent Neural Network, To Process And Interpret Textual Inputs. The LSTM Model Is Trained On A Diverse Dataset Of Text-emotion Pairs To Learn Patterns And Correlations Between Specific Emotions And Corresponding Linguistic Cues. By Employing LSTM, The Chatbot Can Accurately Identify The Underlying Emotions Expressed In User-provided Text. Upon Receiving A Textual Input From The User, The Chatbot Processes The Text And Extracts The Associated Emotions, Which Are Then Used To Recommend Songs That Align With The User's Current Mood. These Song Suggestions Are Retrieved From An Extensive Music Database, Curated To Cater To Various Emotions And Moods. The Music Recommendation Process Leverages Content-based Filtering Techniques To Ensure That The Offered Songs Resonate With The User's Emotional State.

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