Multi-scale sentiment classification using canonical correlation analysis on Riemannian manifolds

Shuanglu Dai, Xingzhong Xu, Bitian Jiang, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Documents with complex sentiment expressions generally pose great challenges in sentiment analysis. This paper proposes a statistical framework to improve sentiment classification within multiscale sentences or paragraphs. A Set of Sentiment Parts (SSP) is first introduced to express sentiment features in different contexts of varying scales. A statistic combination is then determined by analyzing canonical correlations on Riemannian manifolds. A metric learning method is designed to keep the orthogonality within Riemannian point pairs. The nearest neighbor (NN) method is finally used to classify sentiments of SSP. Promising results on various sentiment analysis data sets demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016
Pages144-147
Number of pages4
ISBN (Electronic)9781509045709
DOIs
StatePublished - 18 Jan 2017
Event18th IEEE International Symposium on Multimedia, ISM 2016 - San Jose, United States
Duration: 11 Dec 201613 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016

Conference

Conference18th IEEE International Symposium on Multimedia, ISM 2016
Country/TerritoryUnited States
CitySan Jose
Period11/12/1613/12/16

Keywords

  • Canonical correlation analysis
  • Multi-scale sentiment classification
  • Natural language processing

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