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ACL
2011

Joint Bilingual Sentiment Classification with Unlabeled Parallel Corpora

8 years 2 months ago
Joint Bilingual Sentiment Classification with Unlabeled Parallel Corpora
Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data. We rely on the intuition that the sentiment labels for parallel sentences should be similar and present a model that jointly learns improved monolingual sentiment classifiers for each language. Experiments on multiple data sets show that the proposed approach (1) outperforms the monolingual baselines, significantly improving the accuracy for both languages by 3.44%-8.12%; (2) outperforms two standard approaches for leveraging unlabeled data; and (3) produces (albeit smaller) performance gains when employing pseudo-parallel data from machine translation engines.
Bin Lu, Chenhao Tan, Claire Cardie, Benjamin K. Ts
Added 23 Aug 2011
Updated 23 Aug 2011
Type Journal
Year 2011
Where ACL
Authors Bin Lu, Chenhao Tan, Claire Cardie, Benjamin K. Tsou
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