Feature-based opinion mining is a technique that identifies positive and negative polarity based on object features. The mining technique is different from traditional opinion mining as the feature-based technique does not only examine and summarizes the overall opinion of each review. In smart-phone market, the overall review may not be a practical recommendation for customers to choose their phones. It is essential to summarize opinions of users based on each features of smart-phones. This will be advantage for customers who want to buy smart-phones and smart-phone companies that can use this information 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, first, 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. Then finding similarity word of each feature is performed as 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 70.17% of accuracy.