Pre-processing Task for Classifying Satire in Indonesian News Headline

One of challenges in the topic of natural language processing is detecting satire sentences. In the news headlines, satire commonly used to criticize the goverment. This study aims to know the impact of preprocessing in the classification of satire sentences in Indonesian news headlines. Besides, feature extraction was conducted by using Term Frequency Inverse Document frequency (TF-IDF) employing Machine Learning: Naive Bayes to classify the satire in Indonesian news headline. In thisstudy, there were six preprocessing combinations used which were; breaking down the word into tokens (tokenizing), changing word into base form (stemming), removing stopword, and removing punctuation. The result showed that preprocessing affected the accuracy of sarcastic sentence classification which are not on the dataset of Indonesian news headlines. The highest accuracy of 90.38 obtained by combination of standardized preprocessing which were tokenizing and lowercasing. © 2021 Elsevier B.V., All rights reserved.