Cross-domain sentiment classification using a sentiment sensitive thesaurus

Bollegala, Danushka, Weir, David and Carroll, John (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25 (8). pp. 1719-1731. ISSN 1041-4347

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Abstract

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: P Language and Literature > P Philology. Linguistics > P0098 Computational linguistics. Natural language processing
Q Science > QA Mathematics > QA0076 Computer software
Depositing User: David Weir
Date Deposited: 18 Mar 2013 12:26
Last Modified: 06 Mar 2017 09:20
URI: http://sro.sussex.ac.uk/id/eprint/43452

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