Multistage Sentiment Classification Model using Malaysia Political Ontology

Abstract

Now-a-days, people use social media platforms such as Facebook, Twitter, and Instagram to share their opinions on particular entities or services. The sentiment analysis can get the polarity of these opinions, especially in the political domain. However, in Malaysia, current sentiment analysis can be inaccurate when the netizen tempts to use the combination of Malay words in their comments. It is due to the insufficient Malay corpus and sentiment analysis tools. Therefore, this study aims to construct a multistage sentiment classification model based on Malaysia Political Ontology and Malay Political Corpus. The reviews are carried out in sentiment analysis, classification techniques, Malay sentiment analysis, and sentiment analysis on politics. It starts with the data preparation for Malay tweets to produce tokenized Malay words and then, the construction of corpus using corpus filtering, web search, and filtering using linguistic patterns before enhancing with political lexicons. The process continues with the classifier construction. It started with a generic ontology with Malaysia's political context. Lastly, twelve features are identified. Then the extracted features are tested using different classifiers. As a result, Linear Support Vector Machine yields an accuracy of 86.4% for the classification. It proved that the multistage sentiment classification model improved the Malay tweets classification in the political domain.