Bess or xbest: Mining the Malaysian online reviews

Abstract

Advancement in information and technology facilities especially the Internet has changed the way we communicate and express opinions or sentiments on services or products that we consume. Opinion mining aims to automate the process of mining opinions into the positive or the negative views. It will benefit both the customers and the sellers in identifying the best product or service. Although there are researchers that explore new techniques of identifying the sentiment polarization, few works have been done on opinion mining created by the Malaysian reviewers. The same scenario happens to micro-text. Therefore in this study, we conduct an exploratory research on opinion mining of online movie reviews collected from several forums and blogs written by the Malaysian. The experiment data are tested using machine learning classifiers i.e. Support VectorMachine, Naïve Baiyes and k-Nearest Neighbor. The result illustrates that the performance of these machine learning techniques without any preprocessing of the micro-texts or feature selection is quite low. Therefore additional steps are required in order to mine the opinions from these data.