Ibeke, E., Lin, C. orcid.org/0000-0003-3454-2468, Wyner, A. et al. (1 more author) (2020) A unified latent variable model for contrastive opinion mining. Frontiers of Computer Science, 14 (2). pp. 404-416. ISSN 2095-2228
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. This is an author-produced version of a paper subsequently published in Frontiers of Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | contrastive opinion mining; sentiment analysis; topic modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jan 2020 13:14 |
Last Modified: | 30 Aug 2020 00:38 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/s11704-018-7073-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155564 |