Polarity Analysis of Texts using Discourse Structure

BMWT Heerschop, F Goossen, Alexander Hogenboom, Flavius Frasincar, U Kaymak, Franciska de Jong

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

96 Citations (Scopus)


Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.
Original languageEnglish
Title of host publicationTwentieth ACM Conference on Information and Knowledge Management (CIKM 2011)
Place of PublicationGlasgow, United Kingdom
Number of pages10
ISBN (Print)9781450307178
Publication statusPublished - 24 Oct 2011

Research programs

  • EUR ESE 32


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