@article{DeQueiroz2024,
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.},
author = {de Queiroz, Filipe Augusto Felix and Negreiros, Igor Barbosa and de Souza, Giovana and de Sousa, D{\'{e}}bora Cordeiro and {Xavier J{\'{u}}nior}, S{\'{i}}lvio Fernando Alves},
doi = {https://doi.org/10.51359/2965-4661.2024.265070},
journal = {Socioeconomic Analytics},
keywords = {Logistic Regression,Machine Learning,Natural Language Processing,Random Forest,Sentiment Analysis,Spotify},
number = {1},
pages = {107--113},
title = {{Predicting sentiments in Spotify comments: A comparative analysis of Machine Learning Models}},
url = {https://doi.org/10.51359/2965-4661.2024.265070},
volume = {2},
year = {2024}
}