• DocumentCode
    1827013
  • Title

    Real-time emotion classification of tweets

  • Author

    Janssens, Olivier ; Slembrouck, Maarten ; Verstockt, Steven ; Van Hoecke, Sofie ; Van de Walle, Rik

  • Author_Institution
    Electron. & Inf. Technol. Lab., Ghent Univ., Ghent, Belgium
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    1430
  • Lastpage
    1431
  • Abstract
    Despite adding emotions to applications has proven to enhance the user experience, emotion recognition applications are still not widely available nor used. Within this paper, emotion recognition is done on Twitter tweets using six emotion classification algorithms that are compared on precision and timing. The paper shows that precision can be enhanced by 5.02% compared to the current state-of-the-art by improving the features. Furthermore, the presented algorithms work in real-time.
  • Keywords
    emotion recognition; pattern classification; social networking (online); text analysis; Twitter tweets; emotion classification algorithms; emotion recognition; real-time emotion classification; Emotion recognition; Machine learning algorithms; Real-time systems; Support vector machines; Training; Twitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
  • Conference_Location
    Niagara Falls, ON
  • Type

    conf

  • Filename
    6785892