Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models

Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models

Using data from user sentences on Spotify, this work explores through Natural Language Processing positive and negative sentiments in each comment. We compare different statistical modeling and Machine Learning techniques, identifying the ones with the greatest accuracy in predicting sentiments. As...

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Bibliographic Details
Main Authors: Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros, Giovana de Souza, Débora Cordeiro de Sousa, Sílvio Fernando Alves Xavier Júnior
Format: Article
Language:English
Published: Universidade Federal de Pernambuco (UFPE) 2024-12-01
Series:Socioeconomic Analytics
Subjects:
Natural Language Processing
Spotify
Machine Learning
Logistic Regression
Random Forest
sentiment analysis
Online Access:https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/265070
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Summary:Using data from user sentences on Spotify, this work explores through Natural Language Processing positive and negative sentiments in each comment. We compare different statistical modeling and Machine Learning techniques, identifying the ones with the greatest accuracy in predicting sentiments. As a result, the assessment supports most of the sentences presented with negative connotations. As for modeling, the Logistic Regression and Random Forest models resulted in better accuracy.
ISSN:2965-4661

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