Sentiment Analysis of Telegram App Reviews on Google Play Store Using the Support Vector Machine (SVM) Algorithm

Nofsa Atia Nevrada; Muhammad Adie Syaputra

doi:10.30871/jaic.v9i1.8851

Nofsa Atia Nevrada Sistem Informasi, Universitas Dharma Wacana
Muhammad Adie Syaputra Sistem Informasi, Universitas Dharma Wacana

DOI: https://doi.org/10.30871/jaic.v9i1.8851

Keywords: Data pre-processing, Positive and negative sentiment classification, Sentiment analysis, Support Vector Machine, Telegram application reviews

Abstract

This study aims to analyze the sentiment of Telegram application reviews on the Google Play Store using the Support Vector Machine (SVM) algorithm. From a total of 14,700,000 initial reviews, a sampling technique was carried out to obtain 400 review data, which then went through the pre-processing stage to produce 345 review data to be classified. The SVM model used showed good performance with an accuracy of 81.16%, precision in the positive class reached 93%, recall in the negative class of 94%, and an average f1-score of around 81%. However, there was a discrepancy between the high rating and the content of the review, which highlighted the existence of high-rated reviews that contained criticism or vice versa. The confusion matrix analysis also showed some misclassification, where reviews should be categorized as positive sentiment but detected as negative, and vice versa. This research is expected to provide valuable feedback for Telegram application developers to improve the quality of service, although the results of this analysis have not been fully discussed in practice. The limitation of this study is that it only tested reviews that used Indonesian, which limited the scope of the findings to the context of local users.

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