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 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.

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Publication Details

Journal
Socioeconomic Analytics
Published
2024-12-20
DOI
https://doi.org/10.51359/2965-4661.2024.265070
Citations
1
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.35
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article

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

Sílvio Fernando Alves Xavier Júnior, G. D. G. De Souza, Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros et al.
1 citations
Socioeconomic Analytics
Sentiment Analysis and Opinion Mining
0.35
article

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

Sílvio Fernando Alves Xavier Júnior, G. D. G. De Souza, Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros, Débora Cordeiro de Sousa
article en
1 citations

Abstract

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.

Socioeconomic AnalyticsVol. 2(1)
Universidade Estadual da Paraíba (BR)
Openalex Percentile: Top 30%
Sentiment Analysis and Opinion Mining
0.35
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