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@article{192070,
        author = {Shivaprasad Satla},
        title = {Deep Learning-Based Emotion-Aware Chatbot with Personalized Music Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {246-251},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192070},
        abstract = {The emotion recognition module employs advanced Natural Language Processing techniques using deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and transformer-based architectures. These models are trained on publicly available emotion-labeled datasets, enabling accurate identification of subtle emotional cues within conversational text. The music recommendation component adopts a hybrid approach that combines rule-based emotion-to-genre mapping with collaborative filtering to personalize recommendations based on both emotional context and user preferences. Music data sourced from curated mood-based playlists and streaming platforms supports effective recommendation generation. The chatbot interface facilitates seamless real-time interaction, allowing users to express their emotions naturally while receiving empathetic responses and instant music recommendations. Experimental evaluation shows that the proposed system achieves high accuracy in emotion detection and generates music suggestions that align well with users’ emotional states. Overall, the project demonstrates the effectiveness of deep learning in developing intelligent, emotion-aware chatbots capable of delivering meaningful and personalized user experiences.},
        keywords = {Emotion Recognition, Chatbot System, Music Recommendation, Natural Language Processing, Deep Learning, Personalized User Experience},
        month = {January},
        }