The unified collocation framework for opinion mining

Yun Qing Xia; Rui Feng Xu; Kam Fai Wong; Fang Zheng

doi:10.1109/ICMLC.2007.4370260

The unified collocation framework for opinion mining

Yun Qing Xia^*
, Rui Feng Xu
, Kam Fai Wong
, Fang Zheng

^*Corresponding author for this work

Tsinghua University
Hong Kong Polytechnic University
Chinese University of Hong Kong

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

21 Scopus citations

Abstract

Opinion mining is a complicated text understanding technology involving opinion extraction and sentiment analysis. State-of-the-art techniques adopt idea of attribute-driven or sentiment-driven, leading to low opinion mining coverage. This paper proposes the unified collocation framework (UCF) and describes a novel unified collocation-driven (UCD) opinion mining method. The UCF incorporates attribute-sentiment collocations as well as their syntactical features to achieve reasonable generalization ability. Preliminary experiments show that 0.245 on averages improve recall of opinion extraction without obvious loss on opinion extraction precision and sentiment analysis accuracy.

Original language	English
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	844-850
Number of pages	7
DOIs	https://doi.org/10.1109/ICMLC.2007.4370260
State	Published - 2007
Externally published	Yes
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	2

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

Keywords

Opinion extraction
Opinion mining
Sentiment analysis
Unified collocation

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10.1109/ICMLC.2007.4370260

Cite this

Xia, Y. Q., Xu, R. F., Wong, K. F., & Zheng, F. (2007). The unified collocation framework for opinion mining. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 844-850). Article 4370260 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 2). https://doi.org/10.1109/ICMLC.2007.4370260

@inproceedings{1344d1d4e6e24f8cbb0f09f908e0c68d,

title = "The unified collocation framework for opinion mining",

abstract = "Opinion mining is a complicated text understanding technology involving opinion extraction and sentiment analysis. State-of-the-art techniques adopt idea of attribute-driven or sentiment-driven, leading to low opinion mining coverage. This paper proposes the unified collocation framework (UCF) and describes a novel unified collocation-driven (UCD) opinion mining method. The UCF incorporates attribute-sentiment collocations as well as their syntactical features to achieve reasonable generalization ability. Preliminary experiments show that 0.245 on averages improve recall of opinion extraction without obvious loss on opinion extraction precision and sentiment analysis accuracy.",

keywords = "Opinion extraction, Opinion mining, Sentiment analysis, Unified collocation",

author = "Xia, \{Yun Qing\} and Xu, \{Rui Feng\} and Wong, \{Kam Fai\} and Fang Zheng",

year = "2007",

doi = "10.1109/ICMLC.2007.4370260",

language = "英语",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "844--850",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

Xia, YQ, Xu, RF, Wong, KF & Zheng, F 2007, The unified collocation framework for opinion mining. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370260, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 2, pp. 844-850, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370260

The unified collocation framework for opinion mining. / Xia, Yun Qing; Xu, Rui Feng; Wong, Kam Fai et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 844-850 4370260 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 2).

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

TY - GEN

T1 - The unified collocation framework for opinion mining

AU - Xia, Yun Qing

AU - Xu, Rui Feng

AU - Wong, Kam Fai

AU - Zheng, Fang

PY - 2007

Y1 - 2007

N2 - Opinion mining is a complicated text understanding technology involving opinion extraction and sentiment analysis. State-of-the-art techniques adopt idea of attribute-driven or sentiment-driven, leading to low opinion mining coverage. This paper proposes the unified collocation framework (UCF) and describes a novel unified collocation-driven (UCD) opinion mining method. The UCF incorporates attribute-sentiment collocations as well as their syntactical features to achieve reasonable generalization ability. Preliminary experiments show that 0.245 on averages improve recall of opinion extraction without obvious loss on opinion extraction precision and sentiment analysis accuracy.

AB - Opinion mining is a complicated text understanding technology involving opinion extraction and sentiment analysis. State-of-the-art techniques adopt idea of attribute-driven or sentiment-driven, leading to low opinion mining coverage. This paper proposes the unified collocation framework (UCF) and describes a novel unified collocation-driven (UCD) opinion mining method. The UCF incorporates attribute-sentiment collocations as well as their syntactical features to achieve reasonable generalization ability. Preliminary experiments show that 0.245 on averages improve recall of opinion extraction without obvious loss on opinion extraction precision and sentiment analysis accuracy.

KW - Opinion extraction

KW - Opinion mining

KW - Sentiment analysis

KW - Unified collocation

UR - https://www.scopus.com/pages/publications/37849048324

U2 - 10.1109/ICMLC.2007.4370260

DO - 10.1109/ICMLC.2007.4370260

M3 - 会议稿件

AN - SCOPUS:37849048324

SN - 142440973X

SN - 9781424409730

T3 - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

SP - 844

EP - 850

BT - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

T2 - 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007

Y2 - 19 August 2007 through 22 August 2007

ER -

The unified collocation framework for opinion mining

Abstract

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Keywords

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