Exploiting emoticons in sentiment analysis

Alexander Hogenboom, Daniella Bal, Flavius Frasincar, Malissa Bal, Franciska M.G. de Jong, Uzay Kaymak

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

    146 Citations (Scopus)
    4097 Downloads (Pure)


    As people increasingly use emoticons in text in order to ex- press, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoti-cons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classication method. We evaluate our approach on 2,080 Dutch tweets and forum mes- sages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons signicantly improves sentiment classication accuracy. This indicates that whenever emoticons are used, their associated senti- ment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.
    Original languageUndefined
    Title of host publicationProceedings of the 28th Annual ACM Symposium on Applied Computing, SAC 2013
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages8
    ISBN (Print)978-1-4503-1656-9
    Publication statusPublished - Mar 2013
    Event28th Annual ACM Symposium on Applied Computing, SAC 2013 - Lisbon, Portugal
    Duration: 18 Mar 201322 Mar 2013
    Conference number: 28

    Publication series



    Conference28th Annual ACM Symposium on Applied Computing, SAC 2013
    Abbreviated titleSAC
    Internet address


    • EWI-23268
    • METIS-296399
    • IR-85830

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