A Study of Emotion Classification of Music Lyrics using LSTM Networks

Title

A Study of Emotion Classification of Music Lyrics using LSTM Networks

Creator

Ara A.; Rekha V.

Description

Emotion Recognition is a vital component of human-computer interaction and plays a pivotal role in applications such as sentiment analysis, virtual assistants, and affective computing. Long Short-Term Memory (LSTM) models are a subset of Recurrent Neural Networks (RNNs). It has gained significant popularity for their effectiveness in sequence modeling tasks, including emotion recognition. The study presents a review on the application of Long Short-Term Memory (LSTM) networks for emotion classification using music lyrics. It offers a thorough review of relevant literature and outlines the methodology for implementing LSTM models for emotion recognition. Furthermore, the study emphasizes the significance of hyperparameter tuning in building effective machine-learning models, particularly LSTM-based models. 2024 IEEE.

Source

Proceedings - 2024 5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024, pp. 126-131.

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

emotion; emotion classification; Long Short Term Memory (LSTM); lyric text; music emotion

Coverage

Ara A., Christ University, Dept of Computer Science and Engineering, Kengeri, Bangalore, India; Rekha V., Christ University, Dept of Computer Science and Engineering, Kengeri, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835039523-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICMCSI61536.2024.00026

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