Analyzing App Reviews: A Comparative Evaluation of Machine Learning Algorithms on a Spanish Dataset

Limaylla-Lunarejo, María-Isabel; Condori Fernández, Nelly; Rodríguez Luaces, Miguel

Use this link to cite:

http://hdl.handle.net/2183/40765

Analyzing App Reviews: A Comparative Evaluation of Machine Learning Algorithms on a Spanish Dataset

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XoveTIC_2024_proceedings_Parte10.pdf (332.61 KB)

Identifiers

URI: http://hdl.handle.net/2183/40765

Publication date

2024

Authors

Limaylla-Lunarejo, María-Isabel

Condori Fernández, Nelly

Rodríguez Luaces, Miguel

Abstract

"Currently, the internet plays a main role in collecting and providing information on the needs and preferences of app users. App reviews contain valuable insights, such as bug reports, feature requests, and user feedback. However, manually analyzing these reviews is a time-consuming task. In this paper, we conducted an experiment to automate the process of analyzing app reviews using machine learning algorithms. We utilized and translated the dataset from Gu et al. (2015) to Spanish, which contains approximately 34,000 reviews from several apps. Three algorithms were trained: Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, with hyperparameter optimization performed via Grid Search. Logistic Regression achieved the highest performance with a maximum F1-score of 0.74."