Modeling language discrepancy for cross-lingual sentiment analysis

Qiang Chen, Chenliang Li, Wenjie Li

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

6 Citations (Scopus)

Abstract

Language discrepancy is inherent and be part of human languages. Thereby, the same sentiment would be expressed in different patterns across different languages. Unfortunately, the language discrepancy is overlooked by existing works of cross-lingual sentiment analysis. How to accommodate the inherent language discrepancy in sentiment for better cross-lingual sentiment analysis is still an open question. In this paper, we aim to model the language discrepancy in sentiment expressions as intrinsic bilingual polarity correlations (IBPCs) for better cross-lingual sentiment analysis. Specifically, given a document of source language and its translated counterpart, we firstly devise a sentiment representation learning phase to extract monolingual sentiment representation for each document in this pair separately. Then, the two sentiment representations are transferred to be the points in a shared latent space, named hybrid sentiment space. The language discrepancy is then modeled as a fixed transfer vector under each particular polarity between the source and target languages in this hybrid sentiment space. Two relation-based bilingual sentiment transfer models (i.e., RBST-s, RBST-hp) are proposed to learn the fixped transfer vectors. The sentiment of a target-language document is then determined based on the transfer vector between it and its translated counterpart in the hybrid sentiment space. Extensive experiments over a real-world benchmark dataset demonstrate the superiority of the proposed models against several state-of-the-art alternatives.
Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages117-126
Number of pages10
VolumePart F131841
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Pan Pacific Singapore Hotel, Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Conference

Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Country/TerritorySingapore
CitySingapore
Period6/11/1710/11/17

Keywords

  • Bilingual sentiment transfer model
  • Cross-lingual sentiment analysis
  • Language discrepancy

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this