TY  - JOUR
T1  - Predicting sentiments in Spotify comments: A comparative analysis of Machine Learning Models
A1  - de Queiroz, Filipe Augusto Felix
A1  - Negreiros, Igor Barbosa
A1  - de Souza, Giovana
A1  - de Sousa, Débora Cordeiro
A1  - Xavier Júnior, Sílvio Fernando Alves
Y1  - 2024///
KW  - Sentiment Analysis
KW  - Natural Language Processing
KW  - Machine Learning
KW  - Logistic Regression
KW  - Random Forest
KW  - Spotify
JF  - Socioeconomic Analytics
VL  - 2
IS  - 1
SP  - 107
EP  - 113
DO  - https://doi.org/10.51359/2965-4661.2024.265070
UR  - https://doi.org/10.51359/2965-4661.2024.265070
N2  - 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.
ER  -