An unsupervised aspect detection model for sentiment analysis of reviews

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Third a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Finally two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. The proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques.

Original languageEnglish
Title of host publicationNatural Language Processing and Information Systems - 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013, Proceedings
Pages140-151
Number of pages12
DOIs
Publication statusPublished - 2013
Event18th International Conference on Application of Natural Language to Information Systems, NLDB 2013 - Salford, United Kingdom
Duration: 19 Jun 201321 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7934 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Application of Natural Language to Information Systems, NLDB 2013
Country/TerritoryUnited Kingdom
CitySalford
Period19/06/1321/06/13

Keywords

  • aspect detection
  • opinion mining
  • review mining
  • sentiment analysis

Fingerprint

Dive into the research topics of 'An unsupervised aspect detection model for sentiment analysis of reviews'. Together they form a unique fingerprint.

Cite this