Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences

Ebuka Ibeke, Chenghua Lin, Adam Wyner, Mohamad Hardyman Barawi


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.
Anthology ID:
I17-2067
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
395–400
Language:
URL:
https://aclanthology.org/I17-2067
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences (Ibeke et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2067.pdf