NDLI: Comparison of Model-Based Learning Methods for Feature-Level Opinion Mining

Content Provider	IEEE Xplore Digital Library
Author	Luole Qi Li Chen
Copyright Year	2011
Abstract	The tasks of feature-level opinion mining usually include the extraction of product entities from product reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions' polarities (e.g., positive, negative, or neutral). In recent years, several approaches have been proposed such as rule-based and statistical methods on this subject, but few attentions have been paid to applying more discriminative learning models to achieve the goal. On the other hand, little work has evaluated their algorithms' performance for identifying intensifiers, entity phrases and infrequent entities. In this paper, we in particular adopt the Conditional Random Fields (CRFs) model to perform the opinion mining tasks. Relative to related approaches, we have not only highlighted the algorithm's ability in mining intensifiers, phrases and infrequent entities, but also integrated more elements in the model so as to optimize its training and decoding process. Our method was compared to the lexicalized Hidden Markov Model (L-HMMs) based opinion mining method in the experiment, which proves its significantly better accuracy from several aspects.
Starting Page	265
Ending Page	273
File Size	303212
Page Count	9
File Format	PDF
ISBN	9781457713736
DOI	10.1109/WI-IAT.2011.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training e-Commerce Lexicalized Hidden Markov Model (L-HMMs) Hidden Markov models Conditional Random Fields (CRFs) Feature-Level Opinion Mining Feature extraction Mathematical model Data mining Labeling Equations User Reviews
Content Type	Text
Resource Type	Article

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