Extracting pseudo-labeled samples for sentiment classification using emotion keywords

Sophia Yat Mei Lee*, Daming Dai, Shoushan Li, Kathleen AHRENS

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Sentiment and emotion analysis have been traditionally established as independent research topics in NLP. Although they are two important aspects of subjective information and are closely related, there have been few attempts to combine the two analyses. As a preliminary attempt, we integrate emotion information into sentiment analysis by employing emotion keywords to help automatically extract pseudo-labeled samples. The extracted pseudo-labeled samples are then used as the initial training data to perform semi-supervised learning for sentiment classification. Experimental results across four domains show that our approach using emotion keywords is capable of extracting pseudo-labeled samples with high precision (about 90%). Moreover, the pseudo-labeled samples along with the semi-supervised learning approach further improve the classification performance.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Asian Language Processing, IALP 2011
Pages127-130
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Asian Language Processing, IALP 2011 - Penang, Malaysia
Duration: 15 Nov 201117 Nov 2011

Publication series

NameProceedings - 2011 International Conference on Asian Language Processing, IALP 2011

Conference

Conference2011 International Conference on Asian Language Processing, IALP 2011
Country/TerritoryMalaysia
CityPenang
Period15/11/1117/11/11

Scopus Subject Areas

  • Software

User-Defined Keywords

  • emotion
  • semi-supervised learning
  • sentiment classification

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