论文名称：Learning bilingual sentimentword embeddings for cross-language sentiment classification
论文类型：会议论文
收录刊物：EI、Scopus
卷号：1
页面范围：430-440
摘要：The sentiment classification performance relies on high-quality sentiment resources. However, these resources are imbalanced in different languages. Cross-language sentiment classification (CLSC) can leverage the rich resources in one language (source language) for sentiment classification in a resource-scarce language (target language). Bilingual embeddings could eliminate the semantic gap between two languages for CLSC, but ignore the sentiment information of text. This paper proposes an approach to learning bilingual sentiment word embeddings (BSWE) for English-Chinese CLSC. The proposed BSWE incorporate sentiment information of text into bilingual embeddings. Furthermore, we can learn high-quality BSWE by simply employing labeled corpora and their translations, without relying on largescale parallel corpora. Experiments on NLP&CC 2013 CLSC dataset show that our approach outperforms the state-of-Theart systems. ? 2015 Association for Computational Linguistics.
发表时间：2015-07-26