A unified framework for fine-grained opinion mining from online reviews

Wang, Hao, Zhang, Chen, Yin, Hongzhi, Wang, Wei, Zhang, Jun and Xu, Fanjiang (2016). A unified framework for fine-grained opinion mining from online reviews. In: Proceedings of the 49th Annual Hawaii International Conference on System Sciences, HICSS 2016. 49th Annual Hawaii International Conference on System Sciences, HICSS 2016, Koloa, HI, (1134-1143). 5-8 January 2016. doi:10.1109/HICSS.2016.144


Author Wang, Hao
Zhang, Chen
Yin, Hongzhi
Wang, Wei
Zhang, Jun
Xu, Fanjiang
Title of paper A unified framework for fine-grained opinion mining from online reviews
Conference name 49th Annual Hawaii International Conference on System Sciences, HICSS 2016
Conference location Koloa, HI
Conference dates 5-8 January 2016
Convener IEEE
Proceedings title Proceedings of the 49th Annual Hawaii International Conference on System Sciences, HICSS 2016   Check publisher's open access policy
Journal name Annual Hawaii International Conference on System Sciences. Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher I E E E
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/HICSS.2016.144
Open Access Status Not Open Access
ISBN 9780769556703
ISSN 1530-1605
1060-3425
Volume 2016-March
Start page 1134
End page 1143
Total pages 10
Collection year 2017
Language eng
Abstract/Summary Extracting opinion words and opinion targets from online reviews is an important task for fine-grained opinion mining. Usually, traditional extraction methods under the pipeline-based framework have higher precision but lower recall, while methods in the propagation-based framework possess greater recall but poorer precision. To achieve better performance both in precision and recall, this paper proposes a unified framework for fine-grained opinion mining, combining propagation with refinement in a dynamic and iterative process. In the propagation process, syntactic patterns are chosen as opinion relations to extract new opinion words and targets. Besides, syntactic patterns are further generalized to make them more flexible and scalable. In the refinement process, a three-layer opinion relations graph (ORG) model is constructed based on three types of candidates: opinion word candidates, opinion target candidates and syntactic pattern candidates. A sorting algorithm based on ORG model is proposed to rank all the candidates in their own type, and low-rank candidates are removed from candidate datasets. Repeat propagation and refinement until the syntactic pattern candidate set reaches stable. Experimental results on both English and Chinese online reviews demonstrate the effectiveness of proposed framework and its methods, comparing with the-state-of-the-art methods.
Keyword Opinion words
Opinion targets
Opinion relations graph (ORG)
Fine-grained opinion mining
ORG model
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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