Feature-Based Opinion Mining on Smart Phone Reviews
Mongkol Saensuk, Panida Songram, Phatthanaphong Chomphuwiset · 2015
Feature-based opinion mining was proposed to identify positive and negative polarity of each feature of object. This mining is different from traditional opinion mining which only summarizes overall opinion of each review. For smart phone marketing, we cannot look at only overall opinions. We need to summarize user’s opinions for each feature of smart phone. Then other users can use the summarization to make decision for buying smart phone and smart phone companies can use it to improve features of their smart phone. In this paper, we propose a method for mining opinions on smart phone reviews written in Thai. The method summarizes positive and negative polarity of each feature of smart phones. In this paper, smart phone reviews are collected from smart phone pages on Facebook using Facebook Graph API. Second, the review dataset are clean and then perform word segmentation using a Thai segmentation technique and Path-of-Speech tagging. Then finding similarity word of each feature is performed because the same feature may write the different words. Finally, each feature is decided to be either negative or positive by considering polarity of words which are after the feature. From the experimental result, it was shown than the proposed method gives 79.48% of accuracy.