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

Luole Qi*, Li CHEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
Pages265-273
Number of pages9
DOIs
Publication statusPublished - 2011
Event2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011 - Lyon, France
Duration: 22 Aug 201127 Aug 2011

Publication series

NameProceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
Volume1

Conference

Conference2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011
Country/TerritoryFrance
CityLyon
Period22/08/1127/08/11

Scopus Subject Areas

  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

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

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