Polarity Analysis of Texts using Discourse Structure

Bas Heerschop, Frank Goosen, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska M.G. de Jong

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

    89 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 publicationProceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM2011)
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages10
    ISBN (Print)978-1-4503-0717-8
    Publication statusPublished - Oct 2011
    Event20th ACM International Conference on Information and Knowledge Management, CIKM 2011 - Glasgow, United Kingdom
    Duration: 24 Oct 201128 Oct 2011
    Conference number: 20


    Conference20th ACM International Conference on Information and Knowledge Management, CIKM 2011
    Abbreviated titleCIKM
    Country/TerritoryUnited Kingdom


    • METIS-281555
    • Linguistics
    • Discourse Structure
    • EWI-20782
    • Polarity
    • RST
    • Sentiment
    • IR-78531


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