IMPLEMENTATION OF PREDICTION OF SONG MOOD THROUGH LYRICS - Priyanka Padmane; Krishi Agrahari; Rajsi Kesharwani; Kirti Mohitkar; Mr. Krishi Agrahari; Shazia Khan; Nikhil Kamale (International Journal of Innovations in Engineering Research and Technology, 2022)
Because of the growth of track recordings online [1], the importance of style and emotion type in the music business has long been recognised, or so they believed. Some track player structures, such as Spotify, are known for their track recommendation system, in which they predominantly recommend tracks based on their client’s historical or style choices personally in a large way. Customers receiving suggestions only based on the mood of the lyrics, which is actually quite crucial, might be a very nice idea. Lyrics-primarily based totally evaluation should provide benefits to the track enterprise by robotically tagging the genres and feelings of a song uploaded by essentially means of an artist to generally improve user’s essentially enjoy while attempting to actually find songs in a fairly significant way. The main purpose of this particular experiment is to build an automatic classifier of genres and emotions based entirely on song lyrics, or so they believed. In the experiment, we fine-tuned the pre-trained version and performed switch learning for two types of tasks: style prediction and emotion prediction on a large scale. For all intents and purposes, the version’s input is the song lyrics, and the outputs are largely genre and feeling designations, divided into four categories, or so they believed.