Corse-fine opinion mining

Ruifeng Xu; Chunyu Kit

doi:10.1109/ICMLC.2009.5212768

Corse-fine opinion mining

Ruifeng Xu^*
, Chunyu Kit

^*Corresponding author for this work

City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Most existing opinion mining systems recognize opinionated sentences and determine their polarity as one-step classification procedure. This paper proposes a different multi-pass coarse-fine opinion mining framework. In this framework, a base classifier firstly coarsely estimates the opinion of sentences. The obtained sentence-, paragraph- and document-level opinions are incorporated in an improved classifier as features to re-estimate the opinion of sentences. The updated opinions are feed back to the classifier for further refining the sentence opinion until the classifier outputs converge. Three base classifiers are incorporated in this coarse-fine opinion mining framework, respectively. Their performances are evaluated on NTCIR-6 and NTCIR-7 opinion analysis dataset. The achieved performance improvements show that the proposed coarse-fine strategy is effective to improve the developed opinion mining classifiers.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Pages	3469-3474
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2009.5212768
State	Published - 2009
Externally published	Yes
Event	2009 International Conference on Machine Learning and Cybernetics - Baoding, China Duration: 12 Jul 2009 → 15 Jul 2009

Publication series

Name	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume	6

Conference

Conference	2009 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Baoding
Period	12/07/09 → 15/07/09

Keywords

Classifier
Coarse-fine opinion mining
Opinion analysis
Opinion mining

Access to Document

10.1109/ICMLC.2009.5212768

Cite this

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title = "Corse-fine opinion mining",

abstract = "Most existing opinion mining systems recognize opinionated sentences and determine their polarity as one-step classification procedure. This paper proposes a different multi-pass coarse-fine opinion mining framework. In this framework, a base classifier firstly coarsely estimates the opinion of sentences. The obtained sentence-, paragraph- and document-level opinions are incorporated in an improved classifier as features to re-estimate the opinion of sentences. The updated opinions are feed back to the classifier for further refining the sentence opinion until the classifier outputs converge. Three base classifiers are incorporated in this coarse-fine opinion mining framework, respectively. Their performances are evaluated on NTCIR-6 and NTCIR-7 opinion analysis dataset. The achieved performance improvements show that the proposed coarse-fine strategy is effective to improve the developed opinion mining classifiers.",

keywords = "Classifier, Coarse-fine opinion mining, Opinion analysis, Opinion mining",

author = "Ruifeng Xu and Chunyu Kit",

year = "2009",

doi = "10.1109/ICMLC.2009.5212768",

language = "英语",

isbn = "9781424437030",

series = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

pages = "3469--3474",

booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

note = "2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",

}

Xu, R & Kit, C 2009, Corse-fine opinion mining. in Proceedings of the 2009 International Conference on Machine Learning and Cybernetics., 5212768, Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3469-3474, 2009 International Conference on Machine Learning and Cybernetics, Baoding, China, 12/07/09. https://doi.org/10.1109/ICMLC.2009.5212768

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AU - Xu, Ruifeng

AU - Kit, Chunyu

PY - 2009

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N2 - Most existing opinion mining systems recognize opinionated sentences and determine their polarity as one-step classification procedure. This paper proposes a different multi-pass coarse-fine opinion mining framework. In this framework, a base classifier firstly coarsely estimates the opinion of sentences. The obtained sentence-, paragraph- and document-level opinions are incorporated in an improved classifier as features to re-estimate the opinion of sentences. The updated opinions are feed back to the classifier for further refining the sentence opinion until the classifier outputs converge. Three base classifiers are incorporated in this coarse-fine opinion mining framework, respectively. Their performances are evaluated on NTCIR-6 and NTCIR-7 opinion analysis dataset. The achieved performance improvements show that the proposed coarse-fine strategy is effective to improve the developed opinion mining classifiers.

AB - Most existing opinion mining systems recognize opinionated sentences and determine their polarity as one-step classification procedure. This paper proposes a different multi-pass coarse-fine opinion mining framework. In this framework, a base classifier firstly coarsely estimates the opinion of sentences. The obtained sentence-, paragraph- and document-level opinions are incorporated in an improved classifier as features to re-estimate the opinion of sentences. The updated opinions are feed back to the classifier for further refining the sentence opinion until the classifier outputs converge. Three base classifiers are incorporated in this coarse-fine opinion mining framework, respectively. Their performances are evaluated on NTCIR-6 and NTCIR-7 opinion analysis dataset. The achieved performance improvements show that the proposed coarse-fine strategy is effective to improve the developed opinion mining classifiers.

KW - Classifier

KW - Coarse-fine opinion mining

KW - Opinion analysis

KW - Opinion mining

UR - https://www.scopus.com/pages/publications/70350736120

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DO - 10.1109/ICMLC.2009.5212768

M3 - 会议稿件

AN - SCOPUS:70350736120

SN - 9781424437030

T3 - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

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BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

T2 - 2009 International Conference on Machine Learning and Cybernetics

Y2 - 12 July 2009 through 15 July 2009

ER -

Corse-fine opinion mining

Abstract

Publication series

Conference

Keywords

Access to Document

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