Implementation of Naive Bayes in Sentiment Analysis of CapCut App Reviews on the Play Store

Oka Alvianto; Willy Prihartono; Fathurrohman

doi:10.59934/jaiea.v4i2.805

Authors

Oka Alvianto STMIK IKMI Cirebon
Willy Prihartono STMIK IKMI Cirebon
Fathurrohman STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.805

Keywords:

sentiment analysis, Naive Bayes, CapCut, user reviews, Play Store

Abstract

The CapCut video editing application has gained significant popularity among mobile users. This study aims to analyze user sentiment towards CapCut reviews on the Play Store using the Naive Bayes algorithm. User reviews were collected and preprocessed to clean and prepare the text for analysis. The Naive Bayes algorithm was employed to classify the reviews into positive and negative sentiment categories. Findings indicate that the majority of user reviews are positive, highlighting features such as ease of use, attractive visual effects, and the ability to share videos on social media. However, negative reviews were also identified, primarily criticizing issues like bugs, intrusive advertisements, and limitations in specific features. This research provides valuable insights into user sentiment towards CapCut, which can be utilized by developers to enhance application quality and user experience.

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