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All Journal Journal of Information Systems and Informatics
Chandra Kusuma Dewa
Universitas Islam Indonesia
Computer Science & IT

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Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling Satya Abdul Halim Bahtiar; Chandra Kusuma Dewa; Ahmad Luthfi
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.539

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.
Co-Authors Ahmad Luthfie Satya Abdul Halim Bahtiar