Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification

Bollegala, Danushka, Weir, David and Carroll, John (2011) Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), Portland, Oregon.

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Abstract

We describe a sentiment classication method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to nd the association between words that express similar sentiments in different domains. The created thesaurus is then used to expand feature vectors to train a binary classier. Unlike previous cross-domain sentiment classication methods, our method can efciently learn from multiple source domains. Our method significantly outperforms numerous baselines and returns results that are better than or comparable to previous cross-domain sentiment classication methods on a benchmark dataset containing Amazon user reviews for different types of products.

Item Type: Conference or Workshop Item (Paper)
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: David Weir
Date Deposited: 06 Feb 2012 20:15
Last Modified: 23 Apr 2012 09:21
URI: http://sro.sussex.ac.uk/id/eprint/24985
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