Abstract
Uncovering the latent structure of various hotly discussed topics and the corresponding sentiments from different social media user groups (e.g., Twitter) is critical for helping organizations and governments understand how users feel about their services and facilities, along with the events happening around them. Although numerous research texts have explored sentiment analysis on the different aspects of a product, fewer works have focused on why users like or dislike those products. In this paper, a novel probabilistic model is proposed, namely, the Hierarchical User Sentiment Topic Model (HUSTM), to discover the hidden structure of topics and users while performing sentiment analysis in a unified way. The goal of the HUSTM is to hierarchically model the users’ attitudes (opinions ) using different topic and sentiment information, including the positive, negative, and neutral. The experiment results on real-world data sets show the high quality of the hierarchy obtained by the HUSTM in comparison to those discovered using other state-of-the-art techniques.