Paper: Cross-Lingual Mixture Model for Sentiment Classification

ACL ID P12-1060
Title Cross-Lingual Mixture Model for Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

The amount of labeled sentiment data in En- glish is much larger than that in other lan- guages. Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g. Chinese) using labeled data in the source language (e.g. English). Most existing work relies on machine trans- lation engines to directly adapt labeled data from the source language to the target lan- guage. This approach suffers from the limited coverage of vocabulary in the machine transla- tion results. In this paper, we propose a gen- erative cross-lingual mixture model (CLMM) to leverage unlabeled bilingual parallel data. By fitting parameters to maximize the likeli- hood of the bilingual parallel data, the pro- posed model learns previ...