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Although tailored music recommendation systems have seen major developments in recent years, their integration with user emotions derived from several input modalities is still rather understudied. EmotionInsight and BeatSync is a new music recommendation system presented in this work that dynamically matches musical suggestions with users inferred emotional states derived from both textual and video-based inputs. Using natural language processing (NLP) methods, the system extracts sentiment and emotional context. Face expressions and emotional cues from video frames are analyzed concurrently using deep learning models. Using a multi-modal sentiment fusion technique, the extracted emotional elements are then mapped to a well-chosen music database, so providing context-aware and emotionally resonant song recommendations.
Keywords:
music recommendation, text-based sentiment analysis, image-based sentiment analysis
Cite Article:
"EmotionInsight and BeatSync: a music recommendation system based on sentiment analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a242-a247, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505025.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator