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Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification
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posted on 2023-06-08, 05:38 authored by Danushka Bollegala, David WeirDavid Weir, John CarrollWe 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.
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Publication status
- Published
Page range
132-141Pages
10.0Presentation Type
- paper
Event name
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011)Event location
Portland, OregonEvent type
conferenceDepartment affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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