Cross-Lingual Sentiment Relation Capturing for Cross-Lingual Sentiment Analysis

  • Qiang Chen
  • Wenjie Li
  • Yu Lei
  • Xule Liu
  • Chuwei Luo
  • Yanxiang He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

Sentiment connection is the basis of cross-lingual sentiment analysis (CSLA) solutions. Most of existing work mainly focus on general semantic connection that the misleading information caused by non-sentimental semantics probably lead to relatively low efficiency. In this paper, we propose to capture the document-level sentiment connection across languages (called cross-lingual sentiment relation) for CLSA in a joint two-view convolutional neural networks (CNNs), namely Bi-View CNN (BiVCNN). Inspired by relation embedding learning, we first project the extracted parallel sentiments into a bilingual sentiment relation space, then capture the relation by subtracting them with an error-tolerance. The bilingual sentiment relation considered in this paper is the shared sentiment polarity between two parallel texts. Experiments conducted on public datasets demonstrate the effectiveness and efficiency of the proposed approach.

Keywords

Cross-lingual sentiment relation Bi-View CNN Cross-lingual sentiment analysis 

Notes

Acknowledge

We thank all the anonymous reviewers for their detailed and insightful comments on this paper. The work described in this paper was supported by National Natural Science Foundation of China (61272291, 61672445, 61472290 and 61472291) and The Hong Kong Polytechnic University (G-YBP6, 4-BCB5 and B-Q46C).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qiang Chen
    • 1
  • Wenjie Li
    • 2
  • Yu Lei
    • 2
  • Xule Liu
    • 1
  • Chuwei Luo
    • 1
  • Yanxiang He
    • 1
  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityKowloon, Hong KongChina

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