Jointly Learning Bilingual Sentiment and Semantic Representations for Cross-Language Sentiment Classification

  • Huiwei ZhouEmail author
  • Yunlong Yang
  • Zhuang Liu
  • Yingyu Lin
  • Pengfei Zhu
  • Degen Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)


Cross-language sentiment classification (CLSC) aims at leveraging the semantic and sentiment knowledge in a resource-abundant language (source language) for sentiment classification in a resource-scarce language (target language). This paper proposes an approach to jointly learning bilingual semantic and sentiment representations (BSSR) for English-Chinese CLSC. First, two neural networks are adopted to learn sentence-level sentiment representations in English and Chinese views respectively, which are attached to all word semantic representations in the corresponding sentence to express the words in the certain sentiment context. Then, another two neural networks in two views are designed to jointly learn BSSR of the document from word representations concatenated with their sentence-level sentiment representations. The proposed approach could capture rich sentiment and semantic information in BSSR learning process. Experiments on NLP&CC 2013 CLSC dataset show that our approach is competitive with the state-of-the-art results.


Jointly learning Cross-language Sentiment classification 



This research is supported by Natural Science Foundation of China (No. 61272375).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Huiwei Zhou
    • 1
    Email author
  • Yunlong Yang
    • 1
  • Zhuang Liu
    • 1
  • Yingyu Lin
    • 1
  • Pengfei Zhu
    • 1
  • Degen Huang
    • 1
  1. 1.School of Computer Science and TechnologyDalian University of TechnologyDalianChina

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