A Unified Latent Variable Model for Contrastive Opinion Mining

Ebuka Ibeke, Chenghua Lin* (Corresponding Author), Adam Wyner, Mohamad Hardyman Barawi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
4 Downloads (Pure)


There are large and growing textual corpora in which people express contrastive opinions about the same topic. This has led to an increasing number of studies about contrastive opinion mining. However, there are several notable issues with the existing studies. They mostly focus on mining contrastive opinions from multiple data collections, which need to be separated into their respective collections beforehand. In addition, existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics; this opacity does not help us understand the opinions expressed in the corpus. Finally, contrastive opinion is mostly analysed qualitatively rather than quantitatively. This paper addresses these matters and proposes a novel unified latent variable model (contraLDA), which: mines contrastive opinions from both single and multiple data collections, extracts
the sentences that project the contrastive opinion, and measures the strength of opinion contrastiveness towards the extracted topics. Experimental results show the effectiveness of our model in mining contrasted opinions, which outperformed our baselines in extracting coherent and informative sentiment-bearing topics. We further show the accuracy of our model in classifying topics and sentiments of textual data, and we compared our results to five strong baselines.
Original languageEnglish
Pages (from-to)404-416
Number of pages13
JournalFrontiers of Computer Science
Issue number2
Early online date30 Aug 2019
Publication statusPublished - Apr 2020

Bibliographical note

This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1).


  • Sentiment analysis
  • Topic modelling
  • Contrastive opinion mining
  • sentiment analysis
  • topic modelling
  • contrastive opinion mining


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