Fine-grained product features extraction and categorization in reviews opinion mining

Huang, S; Liu, X; Peng, X; Niu, Z

Fine-grained product features extraction and categorization in reviews opinion mining

Huang, S Liu, X Peng, X

Niu, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012, 2012, pp. 680 - 686
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472	en_US
dc.contributor.author	Niu, Z	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012, 2012, pp. 680 - 686	en_US
dc.identifier.isbn	9780769549255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120877
dc.description.abstract	With the growth of user-generated contents on the Web, product reviews opinion mining increasingly becomes a research practice of great value to e-commerce, search and recommendation. Unfortunately, the number of reviews is rising up to hundreds or even thousands, especially for some popular items, which makes it a laborious work for the potential buyers and the manufacturers to read through them to make a wise decision. Besides, the free format and the uncertainty of reviews expressions, make fine-grained product features extraction and categorization a more difficult task than traditional information extraction techniques. In this work, we propose to treat product feature extraction as a sequence labeling task and employ a discriminative learning model using Conditional Random Fields (CRFs) to tackle it. We innovatively incorporate the part-of-speech features and the sentence structure features into the CRFs learning process. For product feature categorization, we introduce the semantic knowledge-based and distributional context-based similarity measures to calculate the similarities between product feature expressions, then an effective graph pruning based categorizing algorithm is proposed to classify the collection of feature expressions into different semantic groups. The empirical studies have proved the effectiveness and efficiency of our approaches compared with other counterpart methods. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012	en_US
dc.relation.isbasedon	10.1109/ICDMW.2012.53	en_US
dc.title	Fine-grained product features extraction and categorization in reviews opinion mining	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

With the growth of user-generated contents on the Web, product reviews opinion mining increasingly becomes a research practice of great value to e-commerce, search and recommendation. Unfortunately, the number of reviews is rising up to hundreds or even thousands, especially for some popular items, which makes it a laborious work for the potential buyers and the manufacturers to read through them to make a wise decision. Besides, the free format and the uncertainty of reviews expressions, make fine-grained product features extraction and categorization a more difficult task than traditional information extraction techniques. In this work, we propose to treat product feature extraction as a sequence labeling task and employ a discriminative learning model using Conditional Random Fields (CRFs) to tackle it. We innovatively incorporate the part-of-speech features and the sentence structure features into the CRFs learning process. For product feature categorization, we introduce the semantic knowledge-based and distributional context-based similarity measures to calculate the similarities between product feature expressions, then an effective graph pruning based categorizing algorithm is proposed to classify the collection of feature expressions into different semantic groups. The empirical studies have proved the effectiveness and efficiency of our approaches compared with other counterpart methods. © 2012 IEEE.

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http://hdl.handle.net/10453/120877