Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling

Authors

  • Satya Abdul Halim Bahtiar Islamic University of Indonesia, Indonesia
  • Chandra Kusuma Dewa Islamic University of Indonesia, Indonesia
  • Ahmad Luthfi Islamic University of Indonesia, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v5i3.539

Keywords:

Naïve Bayes, Logistic Regression, Marketplace, Google Play Store, Rating-based Labeling

Abstract

This research focuses on sentiment analysis in the marketplace reviews in Google Play Store, a platform for downloading Android applications and providing reviews. Sentiment analysis is essential for understanding user responses to applications, particularly in the app marketplace. In this study, two machine learning algorithms, Naïve Bayes and Logistic Regression, are employed to classify user reviews. The application rating is used as a reference to determine the sentiment of each comment. The dataset is divided into two conditions: using 2 labels (positive & negative) and 3 labels (positive, neutral, & negative). The test results indicate that the highest performance is achieved by classifying with Logistic Regression on the Shopee dataset with 2 labels. The accuracy reaches 84.58%, precision reaches 84.66%, and recall reaches 84.63%. Additionally, the fastest processing time occurs when testing the Lazada 2-label dataset with Naïve Bayes, taking only 0.038 seconds. Overall, the research suggests that datasets with 2 labels tend to yield higher accuracy compared to datasets with 3 labels.

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Published

2023-08-29

Issue

Vol. 5 No. 3 (2023): Journal of Information Systems and Informatics

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How to Cite

[1]
S. A. H. Bahtiar, C. K. Dewa, and A. Luthfi, “Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling”, journalisi, vol. 5, no. 3, pp. 915–927, Aug. 2023, doi: 10.51519/journalisi.v5i3.539.

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