Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences

Emmanuel Ebuka Ibeke, Chenghua Lin, Adam Zachary Wyner, Mohamad Hardyman Bin Barawi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts. Most existing studies separate input data into respective collections. In addition, the relationships between the topics extracted and the sentences in the corpus which express the topics are opaque, hindering our understanding of the opinions expressed in the corpus. We propose a novel unified latent variable model (contraLDA) which addresses the above matters. Experimental results show the effectiveness of our model in mining contrasted opinions, outperforming our baselines.
Original languageEnglish
Title of host publicationProceedings of the The 8th International Joint Conference on Natural Language Processing
Place of PublicationTaiwan
PublisherACL Anthology
Pages395-400
Number of pages6
Volume2
ISBN (Print) 978-1-948087-01-8
Publication statusPublished - Nov 2017
Event8th International Joint Conference on Natural Language Processing (IJCNLP 2017) - Taipei, Taiwan, Province of China
Duration: 27 Nov 20171 Dec 2017

Conference

Conference8th International Joint Conference on Natural Language Processing (IJCNLP 2017)
CountryTaiwan, Province of China
CityTaipei
Period27/11/171/12/17

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  • Cite this

    Ibeke, E. E., Lin, C., Wyner, A. Z., & Barawi, M. H. B. (2017). Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences. In Proceedings of the The 8th International Joint Conference on Natural Language Processing (Vol. 2, pp. 395-400). ACL Anthology. http://aclweb.org/anthology/I17-2067