Cross-lingual sentiment relation capturing for cross-lingual sentiment analysis

Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Chuwei Luo, Yanxiang He

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319566078
Publication statusPublished - 1 Jan 2017
Event39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom
Duration: 8 Apr 201713 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10193 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference39th European Conference on Information Retrieval, ECIR 2017
Country/TerritoryUnited Kingdom


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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