Mood Classification of Bangla Songs Based on Lyrics

TL;DR

This paper develops a machine learning approach using BERT embeddings to classify Bangla song lyrics into four emotional moods, facilitating better emotional connection and understanding of Bangla music.

Contribution

It introduces a novel dataset and applies BERT-based NLP techniques for multi-class mood classification of Bangla songs, which was previously underexplored.

Findings

Achieved accurate multi-class mood classification from lyrics

Compiled a dataset of 4000 Bangla song lyrics with mood labels

Demonstrated the effectiveness of BERT embeddings in emotion detection

Abstract

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.