AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Co-Training Based Bilingual Sentiment Lexicon Learning
Dehong Gao, Furu Wei, Wenjie Li, Xiaohua Liu, Ming Zhou

Last modified: 2013-06-29


In this paper, we address the issue of bilingual sentiment lexicon learning(BSLL) which aims to automatically and simultaneously generate sentiment words for two languages. The underlying motivation is that sentiment information from two languages can perform iterative mutual-teaching in the learning procedure. We propose to develop two classifiers to determine the sentiment polarities of words under a co-training framework, which makes full use of the two-view sentiment information from the two languages. The word alignment derived from the parallel corpus is leveraged to design effective features and to bridge the learning of the two classifiers. The experimental results on English and Chinese languages show the effectiveness of our approach in BSLL.


Bilingual Sentiment Lexicon Learning, Parallel Corpus, Co-training

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