Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs

  • Mike Thelwall
  • Kevan Buckley
  • George Paltoglou
  • Marcin Skowron
  • David Garcia
  • Stephane Gobron
  • Junghyun Ahn
  • Arvid Kappas
  • Dennis Küster
  • Janusz A. Holyst
Conference paper

DOI: 10.1007/978-3-642-37256-8_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)
Cite this paper as:
Thelwall M. et al. (2013) Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg

Abstract

Sentiment analysis programs are now sometimes used to detect patterns of sentiment use over time in online communication and to help automated systems interact better with users. Nevertheless, it seems that no previous published study has assessed whether the position of individual texts within on-going communication can be exploited to help detect their sentiments. This article assesses apparent sentiment anomalies in on-going communication – texts assigned significantly different sentiment strength to the average of previous texts – to see whether their classification can be improved. The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.

Keywords

Sentiment analysis opinion mining social web 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mike Thelwall
    • 1
  • Kevan Buckley
    • 1
  • George Paltoglou
    • 1
  • Marcin Skowron
    • 2
  • David Garcia
    • 3
  • Stephane Gobron
    • 4
  • Junghyun Ahn
    • 5
  • Arvid Kappas
    • 6
  • Dennis Küster
    • 6
  • Janusz A. Holyst
    • 7
  1. 1.Statistical Cybermetrics Research GroupUniversity of WolverhamptonWolverhamptonUK
  2. 2.Austrian Research Institute for Artificial IntelligenceViennaAustria
  3. 3.Chair of Systems DesignETH ZurichZurichSwitzerland
  4. 4.Information and Communication Systems Institute (ISIC)HE-Arc, HES-SOSwitzerland
  5. 5.SCI IC RB GroupEcole polytechnique fédérale de Lausanne EPFLSwitzerland
  6. 6.School of Humanities and Social SciencesJacobs University BremenBremenGermany
  7. 7.Center of Excellence for Complex Systems Research, Faculty of PhysicsWarsaw University of TechnologyWarsawPoland

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