Comparison of Naïve Bayes and Random Forest Algorithm in Webtoon Application Sentiment Analysis

Abstract

The Webtoon application has become one of the popular platforms for reading comics digitally. Webtoons, as a form of digital comics, present various types of comic content. The success of a Webtoon application depends greatly on understanding the preferences and views of its users. User evaluations of Webtoon applications can provide valuable insight into user satisfaction levels, as well as identify problems that need to be fixed by developers. In this research, Sentiment Analysis was applied to user reviews of the Webtoon Application on the Google Play Store. This research uses two different classification algorithms, namely Naïve Bayes and Random Forest, with the aim of comparing their performance in the context of sentiment analysis of user reviews of Webtoon applications. The results of this research are expected to provide an overview of the most suitable algorithm for conducting sentiment analysis classification in Webtoon applications. In collecting the dataset, we involved webtoon user reviews covering various sentiments, such as positive, negative, and neutral. However, in this analysis, the focus is given to two types of sentiment, namely positive and negative. We apply Naïve Bayes and Random Forest algorithms to perform sentiment classification on the reviews. Performance evaluation is carried out by considering metrics such as accuracy, precision, recall, and F1-score. The results of implementing these two algorithms are an accuracy of 74% Naïve Bayes, and 88% Random Forest. It can be concluded that the Random Forest algorithm is superior to the Naïve Bayes algorithm. With this, the Random Forest algorithm becomes a recommendation for classifying sentiment analysis for Webtoon applications with greater accuracy.