Mining product reviews based on shallow dependency parsing

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Mining product reviews based on shallow dependency parsing

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval table of contents

Boston, MA, USA

POSTER SESSION: Posters table of contents

Pages 726-727

Year of Publication: 2009

ISBN:978-1-60558-483-6

Authors

Qi Zhang	Fudan University, Shanghai, China
Yuanbin Wu	Fudan University, Shanghai, China
Tao Li	Florida International University, Miami, FL, USA
Mitsunori Ogihara	University of Miami, Coral Gables, FL, USA
Joseph Johnson	University of Miami, Coral Gables, FL, China
Xuanjing Huang	Fudan University, Shanghai, China

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

This paper presents a novel method for mining product reviews, where it mines reviews by identifying product features, expressions of opinions and relations between them. By taking advantage of the fact that most of product features are phrases, a concept of shallow dependency parsing is introduced, which extends traditional dependency parsing to phrase level. This concept is then implemented for extracting relation between product features and expressions of opinions. Experimental evaluations show that the mining task can benefit from shallow dependency parsing.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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