Acervans/lastfm_RS
LastFM recommendation with sentiment analysis (Bachelor Thesis Project)
This project helps music enthusiasts explore new music by providing song recommendations that consider the emotional tone of music descriptions, album reviews, and other textual content related to artists and tracks. It takes your listening history and preferences (implicitly, through Last.FM data) and processes text using tools like the NRC-VAD Lexicon to suggest music that matches specific emotional qualities. The output is a personalized list of music recommendations, presented through the LastMood web application, for users looking to discover emotionally resonant songs.
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Use this if you are a music lover or enthusiast who wants to discover new songs and artists based on the emotional sentiment expressed in their descriptions, rather than just popularity or genre.
Not ideal if you are looking for a plug-and-play music recommendation service with advanced deep learning models, as it requires local setup and only offers basic recommenders due to size constraints.
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Jan 21, 2025
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