http://dx.doi.org/10.1109/WI-IAT.2011.64">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Comparison of model-based learning methods for feature-level opinion mining

Language

English

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. © 2011 IEEE.

Keywords

Conditional random fields (CRFs), E-commerce, Feature-level opinion mining, Lexicalized Hidden Markov Model (L-HMMs), User reviews

Publication Date

2011

Source Publication Title

Proceedings: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology — Workshops

Start Page

265

End Page

273

Conference Location

Lyon, France

Publisher

IEEE

ISBN (print)

9781457713736

ISBN (electronic)

9780769545134

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