A song emotion identification system from lyrics using heterogeneous ensemble learning

Himadri Mukherjee; Matteo Marciano; Ankita Dhar; Kaushik Roy

doi:10.1109/SILCON59133.2023.10404341

A song emotion identification system from lyrics using heterogeneous ensemble learning

Himadri Mukherjee, Matteo Marciano, Ankita Dhar, Kaushik Roy

Music

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The number of songs is increasing at an explosive rate which has led to the development of automatic song categorization systems. Songs are categorized in different ways like genre, artist, beats per minute (BPM), etc. It is also very important to categorize songs based on their emotional content as the audience often prefers songs of a particular mood. The lyrics of a song become available online instantly after the release of the song itself. This sets the stage for an automated song classification system based on lyrics. In this paper, a system is presented to address this problem. The system was tested with 400 songs from 2 categories namely happy and sad. The dataset was composed of disparate artists, genres, and timelines wherein the highest accuracy of 83.25% was obtained using a heterogeneous ensemble learning-based approach.

Original language	English (US)
Title of host publication	Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350314144
DOIs	https://doi.org/10.1109/SILCON59133.2023.10404341
State	Published - 2023
Event	2023 IEEE Silchar Subsection Conference, SILCON 2023 - Silchar, India Duration: Nov 3 2023 → Nov 5 2023

Publication series

Name	Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023

Conference

Conference	2023 IEEE Silchar Subsection Conference, SILCON 2023
Country/Territory	India
City	Silchar
Period	11/3/23 → 11/5/23

Keywords

Heterogeneous ensemble learning
Song emotion
Song lyrics
Text embedding

ASJC Scopus subject areas

Instrumentation
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Signal Processing
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/SILCON59133.2023.10404341

Cite this

Mukherjee, H., Marciano, M., Dhar, A., & Roy, K. (2023). A song emotion identification system from lyrics using heterogeneous ensemble learning. In Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023 (Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SILCON59133.2023.10404341

A song emotion identification system from lyrics using heterogeneous ensemble learning. / Mukherjee, Himadri; Marciano, Matteo; Dhar, Ankita et al.
Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mukherjee, H, Marciano, M, Dhar, A & Roy, K 2023, A song emotion identification system from lyrics using heterogeneous ensemble learning. in Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023. Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Silchar Subsection Conference, SILCON 2023, Silchar, India, 11/3/23. https://doi.org/10.1109/SILCON59133.2023.10404341

Mukherjee H, Marciano M, Dhar A, Roy K. A song emotion identification system from lyrics using heterogeneous ensemble learning. In Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023). doi: 10.1109/SILCON59133.2023.10404341

Mukherjee, Himadri ; Marciano, Matteo ; Dhar, Ankita et al. / A song emotion identification system from lyrics using heterogeneous ensemble learning. Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023).

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abstract = "The number of songs is increasing at an explosive rate which has led to the development of automatic song categorization systems. Songs are categorized in different ways like genre, artist, beats per minute (BPM), etc. It is also very important to categorize songs based on their emotional content as the audience often prefers songs of a particular mood. The lyrics of a song become available online instantly after the release of the song itself. This sets the stage for an automated song classification system based on lyrics. In this paper, a system is presented to address this problem. The system was tested with 400 songs from 2 categories namely happy and sad. The dataset was composed of disparate artists, genres, and timelines wherein the highest accuracy of 83.25% was obtained using a heterogeneous ensemble learning-based approach.",

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A song emotion identification system from lyrics using heterogeneous ensemble learning

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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