Sentiment Analysis of Duolingo Application Reviews on  Google Play Using the Support Vector Machine Method

Nur Jihan Arifi; Novriyenni; Muammar Khadapi

doi:10.59934/jaiea.v6i1.2809

Authors

Nur Jihan Arifi STMIK Kaputama
Novriyenni STMIK Kaputama
Muammar Khadapi STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v6i1.2809

Keywords:

Sentiment Analysis, Duolingo, Google Play, Support Vector Machine, TF-IDF, CRISP-DM

Abstract

Abstract

The Duolingo app is one of the most widely used language-learning apps available on Google Play. The large number of reviews posted by users makes sentiment analysis necessary to identify trends in user opinions regarding the app—whether positive, negative, or neutral. This study aims to analyze the sentiment of Duolingo app user reviews on Google Play using the Support Vector Machine (SVM) algorithm. Research data was obtained through web scraping of 1,220 user reviews, followed by preprocessing, sentiment labeling, and word weighting using TF-IDF prior to the classification process using the SVM algorithm. The results show that positive sentiment accounted for 57.1%, negative sentiment for 30.3%, and neutral sentiment for 12.6%. Based on the test results, the Support Vector Machine (SVM) algorithm achieved an accuracy of 79.23%, demonstrating fairly good performance in classifying the sentiment of user reviews for the Duolingo app on Google Play.

Keywords: Sentiment Analysis, Duolingo, Google Play, Support Vector Machine (SVM).

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