Naïve bayes and maximum entropy comparison for translated novel’s genre classification

Abstract

Abstract In the last two decades, novel translation had become one of the popular products among the literature community. People had favorited some genre based on their ages. The reader needs to finish reading until the end first before they could determine what genre a novel should have. There were some cases where the genre written in the description differs from the real novel’s content, which made readers felt upset and had not pleasant reading experience. This research is going to do classification for the novel’s genre automatically. Naïve Bayes is the method chosen for classification, later the result of Naïve Bayes classification is going to be compared with another algorithm, which is Maximum Entropy algorithm. Each method would apply algorithms to label the data based on an existing class. The data origin was taken from 12 translated novel that has 3746 lines. Data was portioned into three genre classes, “Action-Fantasy” for about 1293 lines, “Modern-Slice-of-Life” for 1203 lines, and 1250 for “Other”. Evaluation of the two models, Naïve Bayes and MaxEnt, would be using confusion matrix that generated the highest number precision, recall, and F-score which values were 77,52%; 75,59%; and 77,55% for the Naïve Bayes method, and 78,11%; 83,82%; and 75,81% for MaxEnt method. The value of accuracy were 72,72% for Naïve Bayes, and 71,86% for MaxEnt. Both methods showed that the genre “Action-Fantasy” was the correct genre for almost every novel among 12 novels listed.