Sentiment Analysis of User Reviews of the CapCut Application on Google Play Store Using the Naive Bayes Algorithm
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Abstract
The rapid growth of digital technology has increased the use of video editing applications, including CapCut, which is highly popular among Android users. However, a high number of downloads does not necessarily reflect user satisfaction. This study aims to analyze user sentiment toward CapCut based on reviews from the Google Play Store. A total of 500 reviews were collected using the google-play-scraper Python library and automatically labeled based on rating values. The data were processed through text preprocessing, TF-IDF weighting, and sentiment classification using the Multinomial Naive Bayes algorithm. Sentiments were categorized into positive, neutral, and negative classes. The results show that positive sentiment dominates with 54.2%, followed by negative sentiment at 38.8% and neutral sentiment at 7%. The model achieved an accuracy of 81.72% with balanced precision and recall values. Overall, most users express positive perceptions of CapCut, although complaints regarding advertisements and paid features remain notable.
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