Mood Classification of Bangla Songs Based on Lyrics
This work addresses a gap in research on Bangla music by enabling multi-class mood classification, making music more relatable to emotions, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of classifying Bangla songs into moods based on lyrics by compiling a dataset of 4000 songs and using NLP with the BERT algorithm, resulting in automated classification into four moods (e.g., 1513 sad, 1362 romantic).
Music can evoke various emotions, and with the advancement of technology, it has become more accessible to people. Bangla music, which portrays different human emotions, lacks sufficient research. The authors of this article aim to analyze Bangla songs and classify their moods based on the lyrics. To achieve this, this research has compiled a dataset of 4000 Bangla song lyrics, genres, and used Natural Language Processing and the Bert Algorithm to analyze the data. Among the 4000 songs, 1513 songs are represented for the sad mood, 1362 for the romantic mood, 886 for happiness, and the rest 239 are classified as relaxation. By embedding the lyrics of the songs, the authors have classified the songs into four moods: Happy, Sad, Romantic, and Relaxed. This research is crucial as it enables a multi-class classification of songs' moods, making the music more relatable to people's emotions. The article presents the automated result of the four moods accurately derived from the song lyrics.