As more and more Thais own mobile devices, mobile applications are high in demand. Before installing mobile applications, many users read reviews written by other users to determine whether or not the application is worth using. In addition, mobile application developers also rely on user reviews to get insight information on which aspects of the mobile application users like or do not like and why. They can use the information to market the beloved aspects of their software product and improve on the problematic ones. However, when there are many reviews, it is difficult to comprehend information in the user reviews. Several researches in recent years aim to extract opinions and sentiments from various texts or documents such as Twitter, webboards, and software product reviews. Most of these researches are for English documents. For Thai language, researches usually focus on other contexts such as hotel reviews or general opinions on Twitter. In this paper, we present an approach to analyze user reviews written in Thai based on techniques in natural language processing, topic modeling, and sentiment analysis. The approach aims to help Thai users and developers discover dynamically, instead of pre-determined, various aspects and associated sentiments from a vast amount of user reviews. The result of the approach is a list of aspects with associated opinions and sentiments to help users assess mobile applications and provide summarized user feedbacks for developers.