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        <title>Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences</title>
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        <namePart type="given">Ebuka</namePart>
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        <namePart type="given">Chenghua</namePart>
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences
%A Ibeke, Ebuka
%A Lin, Chenghua
%A Wyner, Adam
%A Barawi, Mohamad Hardyman
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 nov
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F ibeke-etal-2017-extracting
%X 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.
%U https://aclanthology.org/I17-2067
%P 395-400

Markdown (Informal)

[Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences](https://aclanthology.org/I17-2067) (Ibeke et al., IJCNLP 2017)

• Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences (Ibeke et al., IJCNLP 2017)

ACL

• Ebuka Ibeke, Chenghua Lin, Adam Wyner, and Mohamad Hardyman Barawi. 2017. Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 395–400, Taipei, Taiwan. Asian Federation of Natural Language Processing.