PlayReviewsPT: Um Dataset de 97 Milhões de Avaliações do Google Play em Português Brasileiro
Resumo
Este trabalho apresenta o PlayReviewsPT, um dataset público com 97,2 milhões de avaliações em português brasileiro de 96 aplicativos do Google Play (2010–2025). Os dados são disponibilizados em formato JSONL, particionados por aplicativo, com metadados estruturados e o código do coletor. O artigo descreve a metodologia de coleta, o esquema dos dados e uma caracterização inicial. As análises mostram concentração em poucos aplicativos, forte polarização das notas, predominância de comentários curtos e crescimento expressivo do volume ao longo do tempo. O PlayReviewsPT é um recurso em larga escala para processamento de linguagem natural, mineração de avaliações de aplicativos e engenharia de software empírica.
Palavras-chave:
Mineração de Avaliações de Aplicativos, Dataset em Português Brasileiro, Engenharia de Software Empírica
Referências
Di Sorbo, A., Panichella, S., Alexandru, C. V., Shimagaki, J., Visaggio, C. A., Canfora, G., and Gall, H. C. (2016). What would users change in my app? summarizing app reviews for recommending software changes. In Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering, pages 499–510.
Genc-Nayebi, N. and Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125:207– 219.
Harman, M., Jia, Y., and Zhang, Y. (2012). App store mining and analysis: Msr for app stores. In 2012 9th IEEE working conference on mining software repositories (MSR), pages 108–111. IEEE.
Iacob, C. and Harrison, R. (2013). Retrieving and analyzing mobile apps feature requests from online reviews. In 2013 10th working conference on mining software repositories (MSR), pages 41–44. IEEE.
Khan, N. D., Khan, J. A., Li, J., Ullah, T., and Zhao, Q. (2024). Mining software insights: uncovering the frequently occurring issues in low-rating software applications. PeerJ Computer Science, 10:e2115.
Pagano, D. and Maalej, W. (2013). User feedback in the appstore: An empirical study. In 2013 21st IEEE international requirements engineering conference (RE), pages 125–134. IEEE.
Panichella, A., Dit, B., Oliveto, R., Di Penta, M., Poshynanyk, D., and De Lucia, A. (2013). How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. In 2013 35th International conference on software engineering (ICSE), pages 522–531. IEEE.
Pereira, D. A. (2021). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Piau, M., Lotufo, R., and Nogueira, R. (2024). ptt5-v2: A closer look at continued pretraining of t5 models for the portuguese language. In Brazilian Conference on Intelligent Systems, pages 324–338. Springer.
Samanmali, P. and Rupasingha, R. A. (2024). Sentiment analysis on google play store app users’ reviews based on deep learning approach. Multimedia Tools and Applications, 83(36):84425–84453.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: pretrained bert models for brazilian portuguese. In Brazilian conference on intelligent systems, pages 403–417. Springer.
Genc-Nayebi, N. and Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125:207– 219.
Harman, M., Jia, Y., and Zhang, Y. (2012). App store mining and analysis: Msr for app stores. In 2012 9th IEEE working conference on mining software repositories (MSR), pages 108–111. IEEE.
Iacob, C. and Harrison, R. (2013). Retrieving and analyzing mobile apps feature requests from online reviews. In 2013 10th working conference on mining software repositories (MSR), pages 41–44. IEEE.
Khan, N. D., Khan, J. A., Li, J., Ullah, T., and Zhao, Q. (2024). Mining software insights: uncovering the frequently occurring issues in low-rating software applications. PeerJ Computer Science, 10:e2115.
Pagano, D. and Maalej, W. (2013). User feedback in the appstore: An empirical study. In 2013 21st IEEE international requirements engineering conference (RE), pages 125–134. IEEE.
Panichella, A., Dit, B., Oliveto, R., Di Penta, M., Poshynanyk, D., and De Lucia, A. (2013). How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. In 2013 35th International conference on software engineering (ICSE), pages 522–531. IEEE.
Pereira, D. A. (2021). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
Piau, M., Lotufo, R., and Nogueira, R. (2024). ptt5-v2: A closer look at continued pretraining of t5 models for the portuguese language. In Brazilian Conference on Intelligent Systems, pages 324–338. Springer.
Samanmali, P. and Rupasingha, R. A. (2024). Sentiment analysis on google play store app users’ reviews based on deep learning approach. Multimedia Tools and Applications, 83(36):84425–84453.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: pretrained bert models for brazilian portuguese. In Brazilian conference on intelligent systems, pages 403–417. Springer.
Publicado
08/09/2026
Como Citar
DE MELO, Tiago.
PlayReviewsPT: Um Dataset de 97 Milhões de Avaliações do Google Play em Português Brasileiro. In: DATASET SHOWCASE WORKSHOP (DSW), 8. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 72-82.
DOI: https://doi.org/10.5753/dsw.2026.249505.
