Sentiment Analysis of Free Online Novel Applications Using the Support Vector Machine Method - Yulidayanti, Yulidayanti -; Safwandi, Safwandi; Fajriana, Fajriana (International Journal of Engineering, Science and Information Technology, 2025)
Sentiment analysis is a study to analyze opinions and perceptions of various topics, products, or services. With the advancement of technology, people now have easier access to literary works online, including novels. The shift from offline to online reading has resulted in a large volume of review data, necessitating an automated system to classify this data. This research aims to analyze the sentiment of reviews for online novel applications using the Support Vector Machine (SVM) algorithm. The data used in this study was gathered from user reviews of the Wattpad, Noveltoon, and Joylada applications downloaded from the Google Play Store. The results show that the Wattpad application achieved 63% accuracy, 50% precision, 64% recall, and 56% F1-score, with a 41% positive and 59% negative sentiment distribution. The Noveltoon application achieved 70% accuracy, 69% precision, 73% recall, and 71% F1 score, with a 48% positive and 52% negative sentiment distribution. The Joylada application recorded 67% accuracy, 55% precision, 92% recall, and 69% F1-score, with a 57% positive and 43% negative sentiment distribution. The results of this analysis can help understand user preferences towards online novel applications and provide insights into their impact on the application's image and user interactions.