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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Federal de Pernambuco (UFPE)
2024-12-01
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| Series: | Socioeconomic Analytics |
| Subjects: | |
| Online Access: | https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/265070 |
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| author | Filipe Augusto Felix de Queiroz Igor Barbosa Negreiros Giovana de Souza Débora Cordeiro de Sousa Sílvio Fernando Alves Xavier Júnior |
| author_facet | Filipe Augusto Felix de Queiroz Igor Barbosa Negreiros Giovana de Souza Débora Cordeiro de Sousa Sílvio Fernando Alves Xavier Júnior |
| author_sort | Filipe Augusto Felix de Queiroz |
| collection | DOAJ |
| description |
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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| format | Article |
| id | doaj-art-c56b27daa7cc4f23bc8a0473784e7183 |
| institution | Kabale University |
| issn | 2965-4661 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Universidade Federal de Pernambuco (UFPE) |
| record_format | Article |
| series | Socioeconomic Analytics |
| spelling | doaj-art-c56b27daa7cc4f23bc8a0473784e71832025-02-07T17:46:09ZengUniversidade Federal de Pernambuco (UFPE)Socioeconomic Analytics2965-46612024-12-012110.51359/2965-4661.2024.265070Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning ModelsFilipe Augusto Felix de Queiroz0https://orcid.org/0009-0008-4459-6914Igor Barbosa Negreiros1https://orcid.org/0009-0009-4149-4581Giovana de Souza2https://orcid.org/0009-0000-1050-3574Débora Cordeiro de Sousa3https://orcid.org/0009-0006-1410-4718Sílvio Fernando Alves Xavier Júnior4https://orcid.org/0000-0002-4832-0711State University of ParaíbaState University of ParaíbaState University of ParaíbaState University of ParaíbaState University of Paraíba 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. https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/265070Natural Language ProcessingSpotifyMachine LearningLogistic RegressionRandom Forestsentiment analysis |
| spellingShingle | Filipe Augusto Felix de Queiroz Igor Barbosa Negreiros Giovana de Souza Débora Cordeiro de Sousa Sílvio Fernando Alves Xavier Júnior Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models Socioeconomic Analytics Natural Language Processing Spotify Machine Learning Logistic Regression Random Forest sentiment analysis |
| title | Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models |
| title_full | Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models |
| title_fullStr | Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models |
| title_full_unstemmed | Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models |
| title_short | Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models |
| title_sort | predicting sentiments in spotify comments a comparative analysis of machine learning models |
| topic | Natural Language Processing Spotify Machine Learning Logistic Regression Random Forest sentiment analysis |
| url | https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/265070 |
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