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Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews

Bagheri, A, Saraee, MH and de Jong, F 2013, 'Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews' , Knowledge-Based Systems, 52 (2013) , pp. 201-213.

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With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the internet, 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 these aspects. In this paper, we propose a novel unsupervised and domain-independent model for detecting explicit and implicit aspects in reviews for sentiment analysis. In the model, first a generalized method is proposed to learn multi-word aspects and then a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Second 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. Third, two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. Finally the model employs an approach which uses explicit aspects and opinion words to identify implicit aspects. Utilizing extracted polarity lexicon, the approach maps each opinion word in the lexicon to the set of pre-extracted explicit aspects with a co-occurrence metric. The proposed model was evaluated on a collection of English product review datasets. The model does not require any labelled training data and it can be easily applied to other languages or other domains such as movie reviews. Experimental results show considerable improvements of our model over conventional techniques including unsupervised and supervised approaches.

Item Type: Article
Themes: Memory, Text and Place
Schools: Schools > School of Computing, Science and Engineering
Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Knowledge-Based Systems
Publisher: Elsevier
Refereed: Yes
ISSN: 0950-7051
Related URLs:
Funders: Non funded research
Depositing User: Dr Mo Saraee
Date Deposited: 06 Nov 2013 10:55
Last Modified: 30 Nov 2015 23:54

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