Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Twitter is a popular social media platform in Malaysia that allows for 280-character microblogging. Almost everything that happens in a single day is tweeted by users. Because of the popularity of Twitter, most Malaysians use it daily, providing researchers and developers with a wealth of data on Malaysian users. This paper explains why and how this study chose to create a new Malay Twitter corpus, Malay Part-of-Speech (POS) tags, and a Malay POS tagger model. The goal of this paper is to improve existing Malay POS tags so that they are more compatible with the newly created Malay Twitter corpus, as well as to build a POS tagging model specifically tailored for Malay Twitter data using various machine learning algorithms. For instance, Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) classifiers. This study’s data was gathered by using Twitter's Advanced Search function and relevant and related keywords associated with informal Malay. The data was fed into machine learning algorithms after several stages of processing to serve as the training and testing corpus. The evaluation and analysis of the developed Malay POS tagger model show that the SVM classifier, as well as the newly proposed Malay POS tags, is the best machine learning algorithm for Malay Twitter data. Furthermore, the prediction accuracy and POS tagging results show that this research outperformed a comparable previous study, indicating that the Malay POS tagger model and its POS were successfully improved.