Acknowledging discourse function for sentiment analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Naïve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing
Subtitle of host publication15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II
EditorsAlexander Gelbukh
PublisherSpringer
Pages45-52
Number of pages8
Volume8404 LNCS
ISBN (Electronic)9783642549038
ISBN (Print)9783642549021
DOIs
Publication statusPublished - 2014
EventComputational Linguistics and Intelligent Text Processing, 15th International Conference, CICLing 2014 - Kathmandu, Nepal, Nepal
Duration: 6 Apr 201412 Apr 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8404
ISSN (Print)0302-9743

Conference

ConferenceComputational Linguistics and Intelligent Text Processing, 15th International Conference, CICLing 2014
Country/TerritoryNepal
CityKathmandu, Nepal
Period6/04/1412/04/14

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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