Opinion Mining of Thai Politics on Facebook Status Updates
Panida Songram, Chatklaw Jareanpon · 2015
Currently, social networking websites are widely used for communication among people. A multitude of websites are available for sharing ideas and knowledge. Facebook is the most popular social networking website in the world. Thai people widely use status updates on Facebook for all types of discussion topics: political issues, religious issue, technology, education, etc. The discussion topics express their opinion in the form of text. The opinion is very important part of making any decision. This paper is proposed to mine opinion of Thai people about the current government revolution. The opinion was extracted from the Facebook statuses updates which were written in Thai language. Two feature extraction methods were implemented. First, the traditional pre-process of text mining was used for extracting the features. Second, positive and negative words were collected to construct a sentiment lexicon to extract features. Comparative experiments were performed among Naïve Bayes, SVM (Support Vector Machines), KNN (K-Nearest-Neighbor) and decision trees. From the experimental results, they were shown that KNN gave highest accuracy when using the lexicon for feature extraction.