Abstract
In recent years, there has been a rapid growth of research interest in natural language processing that seeks to better understand sentiment or opinion expressed in text. There are several notable issues in most previous work in sentiment analysis, among them: the trained classifiers are domain-dependent; the labeled corpora required for training can be difficult to acquire from real-world text; and dependencies between sentiments and topics are not taken into consideration. In response to these limitations, a new family of probabilistic topic models, namely joint sentiment-topic models, have been developed, which are capable of detecting sentiment in connection with topic from text without using any labeled data for training. In addition, the sentiment-bearing topics extracted by the joint sentiment-topic models provide means for automatically discovering and summarizing opinions from a vast amount of user-generated data. (C) 2015 John Wiley & Sons, Ltd.