Analisis Sentimen Ulasan Aplikasi HeyJapan di Google Play Store Menggunakan Algoritma NLP

Jasmine Aulia Mumtaz; Kinaya Khairunnisa Komariansyah; Wildan Holik; Muhammad Galuh Gumelar; Reza Pratama; Humannisa Rubina Lestari

doi:10.61132/pragmatik.v3i3.1801

Authors

Jasmine Aulia Mumtaz IPB University
Kinaya Khairunnisa Komariansyah IPB University
Wildan Holik IPB University
Muhammad Galuh Gumelar IPB University
Reza Pratama IPB University
Humannisa Rubina Lestari IPB University https://orcid.org/0000-0002-2385-9382

DOI:

https://doi.org/10.61132/pragmatik.v3i3.1801

Keywords:

Sentiment Analysis, HeyJapan, Natural Language Processing, TextBlob, Logistic Regression

Abstract

Digital learning applications like HeyJapan are increasingly popular. User reviews on platforms such as Google Play Store contain valuable information on user perceptions and experiences. To process this information systematically, this study employs a Natural Language Processing (NLP) approach to analyze sentiment toward the HeyJapan application. Data was collected using web scraping techniques with Python and the google play scraper library, resulting in 1,000 latest user reviews. The analysis included data collection, preprocessing, sentiment labeling using TextBlob, visualization, modeling with Logistic Regression, and evaluation. After preprocessing, 923 valid reviews were classified into three sentiment categories based on polarity which are positive, neutral, and negative. Results showed 71.4% of reviews positive, 26.1% neutral, and 2.5% negative. Visualizations in pie charts and word clouds provided an overview of user perceptions. Modeling with TF-IDF and Logistic Regression achieved 88% accuracy with the highest f1-score in the positive sentiment category. Evaluation indicates the model is fairly reliable in classifying sentiments, especially for positive and neutral categories, though negative sentiment classification needs improvement. This study shows the NLP approach can evaluate user perceptions of educational applications based on reviews and serve as a basis for improving foreign language learning app quality.

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