Sentiment-Topic Modelling in Text Mining

Chenghua Lin, Emmanuel Ebuka Ibeke, Adam Zachary Wyner, Frank Guerin

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
8 Downloads (Pure)


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. WIREs Data Mining Knowl Discov 2015, 5:246–254. doi: 10.1002/widm.1161
Original languageEnglish
Pages (from-to)246-254
Number of pages9
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Issue number5
Early online date14 Aug 2015
Publication statusPublished - Sept 2015


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