Mining Opinion in Online Messages

The number of messages that can be mined from online entries increases as the number of online application users increases. In Malaysia, online messages are written in mixed languages known as ‘Bahasa Rojak’. Therefore, mining opinion using natural language processing activities is difficult. This study introduces a Malay Mixed Text Normalization Approach (MyTNA) and a feature selection technique based on Immune Network System (FS-INS) in the opinion mining process using machine learning approach. The purpose of MyTNA is to normalize noisy texts in online messages. In addition, FS-INS will automatically select relevant features for the opinion mining process. Several experiments involving 1000 positive movies feedback and 1000 negative movies feedback have been conducted. The results show that accuracy values of opinion mining using Naïve Bayes (NB), k-Nearest Neighbor (kNN) and Sequential Minimal Optimization (SMO) increase after the introduction of MyTNA and FS-INS. Keywords—Opinion mining; text normalization; feature selection.