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@article{186568,
        author = {Amrit Gupta and Mohammed Tufail and Jainil Solanki and Karan Tirwa},
        title = {Emotion-Based Bollywood Music Recommendation Systems: Integrating Facial Expression Recognition and Lyrics Sentiment Analysis—A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1808-1812},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186568},
        abstract = {Emotion-based music recommendation systems are transforming the way users interact with large-scale digital music libraries, particularly in emotionally rich domains like Bollywood music. This review surveys current advances in integrating facial expression recognition (FER) and lyrics sentiment analysis, emphasizing their application in the Indian music landscape. We explore deep learning, transformer-based approaches for Hindi/English lyric emotion tagging, multimodal fusion (facial+ audio + text), system architectures, and practical challenges for real-time use. Ten pivotal research papers from 2016–2025 are analyzed comparatively, with a focus on scope, methodology, efficacy, and limitations. Our review concludes with practical directions for future research on personalized, affective music recommendation in Indian languages, stressing the need for culturally attuned models and real-time efficiency.},
        keywords = {Emotion Recognition, Music Recommendation, Lyrics Sentiment, Bollywood, Facial Expression, Hindi, Trans- former, Deep Learning, Multimodal, Review},
        month = {November},
        }