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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)


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.


Sentiment analysis opinion mining social web 


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  1. 1.
    Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of WWW, pp. 529–535 (2003) Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (2010), (retrieved May 25, 2010)
  3. 3.
    Bollen, J., Pepe, A., Mao, H.: Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena. In: ICWSM 2011, Barcelona, Spain (2011), (retrieved June 2, 2011)
  4. 4.
    Chmiel, A., Sienkiewicz, J., Thelwall, M., Paltoglou, G., Buckley, K., Kappas, A., Hołyst, J.A.: Collective emotions online and their influence on community life. PLoS ONE 6(7), e22207 (2011a)CrossRefGoogle Scholar
  5. 5.
    Chmiel, A., Sienkiewicz, J., Paltoglou, G., Buckley, K., Thelwall, M., Holyst, J.A.: Negative emotions boost user activity at BBC forum. Physica A 390(16), 2936–2944 (2011b)CrossRefGoogle Scholar
  6. 6.
    Choi, Y., Cardie, C.: Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 793–801 (2008)Google Scholar
  7. 7.
    Cornelius, R.R.: The science of emotion. Prentice Hall, Upper Saddle River (1996)Google Scholar
  8. 8.
    Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies 11(4), 441–456 (2010)CrossRefGoogle Scholar
  9. 9.
    Fox, E.: Emotion science. Palgrave Macmillan, Basingstoke (2008)Google Scholar
  10. 10.
    Garas, A., Garcia, D., Skowron, M., Schweitzer, F.: Emotional persistence in online chatting communities. Scientific Reports 2, article 402 (2012), doi:10.1038/srep00402Google Scholar
  11. 11.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011) (2011)Google Scholar
  12. 12.
    Gobron, S., Ahn, A., Silvestre, Q., Thalmann, D., Rank, S., Skowron, M., Thelwall, M.: An interdisciplinary VR-architecture for 3D chatting with non-verbal communication. In: Proceedings of the Joint Virtual Reality Conference of EuroVR (EGVE 2011), Nottingham, UK, pp. 87–94 (2011)Google Scholar
  13. 13.
    Kramer, A.D.I.: An unobtrusive behavioral model of “gross national happiness”. In: Proceedings of CHI 2010, pp. 287–290. ACM Press, New York (2010)Google Scholar
  14. 14.
    Krippendorff, K.: Content analysis: An introduction to its methodology. Sage, Thousand Oaks (2004)Google Scholar
  15. 15.
    Kucuktunc, O., Cambazoglu, B.B., Weber, I., Ferhatosmanoglu, H.: A large-scale sentiment analysis for Yahoo! Answers. Paper Presented at the Web Search and Data Mining (WSDM 2012), Seattle, Washington, pp. 633–642 (2012)Google Scholar
  16. 16.
    Liu, B.: Sentiment analysis and opinion mining. Morgan and Claypool, New York (2012)Google Scholar
  17. 17.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Recognition of fine-grained emotions from text: An approach based on the compositionality principle. In: Nishida, T., Jain, L., Faucher, C. (eds.) Modelling Machine Emotions for Realizing Intelligence: Foundations and Applications, pp. 179–207 (2010)Google Scholar
  18. 18.
    Norman, G.J., Norris, C., Gollan, J., Ito, T., Hawkley, L., Larsen, J., Berntson, G.G.: Current emotion research in psychophysiology: The neurobiology of evaluative bivalence. Emotion Review 3, 3349–3359 (2011), doi:10.1177/1754073911402403CrossRefGoogle Scholar
  19. 19.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 1(1-2), 1–135 (2008)CrossRefGoogle Scholar
  20. 20.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86. ACL, Morristown (2002)Google Scholar
  21. 21.
    Pennebaker, J., Mehl, M., Niederhoffer, K.: Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology 54, 547–577 (2003)CrossRefGoogle Scholar
  22. 22.
    Ponomareva, N., Thelwall, M.: Do neighbours help? an exploration of graph-based algorithms for cross-domain sentiment classification. In: The 2012 Conference on Empirical Methods on Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012) (2012)Google Scholar
  23. 23.
    Skowron, M.: Affect listeners: Acquisition of affective states by means of conversational systems. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) Second COST 2102. LNCS, vol. 5967, pp. 169–181. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  24. 24.
    Skowron, M., Pirker, H., Rank, S., Paltoglou, G., Ahn, J., Gobron, S.: No peanuts! Affective cues for the virtual bartender. In: Murray, R.C., McCarthy, P.M. (eds.) Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS-24), pp. 117–122. AAAI Press, Menlo Park (2011)Google Scholar
  25. 25.
    Somasundaran, S., Namata, G., Wiebe, J., Getoor, L.: Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. In: Empirical Methods in Natural Language Processing (EMNLP 2009), pp. 170–179 (2009)Google Scholar
  26. 26.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Computational Linguistics 37(2), 267–307 (2011)CrossRefGoogle Scholar
  27. 27.
    Thelwall, M., Buckley, K.: Topic-based sentiment analysis for the social web: The role of mood and issue-related words. Journal of the American Society for Information Science and Technology (in press)Google Scholar
  28. 28.
    Thelwall, M.: Emotion homophily in social network site messages. First Monday 10(4) (2010), (retrieved March 6, 2011)
  29. 29.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in twitter events. Journal of the American Society for Information Science and Technology 62(2), 406–418 (2011)CrossRefGoogle Scholar
  30. 30.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology 63(1), 163–173 (2012)CrossRefGoogle Scholar
  31. 31.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  32. 32.
    Thelwall, M., Sud, P., Vis, F.: Commenting on YouTube videos: From Guatemalan rock to el big bang. Journal of the American Society for Information Science and Technology 63(3), 616–629 (2012)CrossRefGoogle Scholar
  33. 33.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, July 6-12, pp. 417–424 (2002)Google Scholar
  34. 34.
    Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Computational Linguistics 30(3), 277–308 (2004)CrossRefGoogle Scholar
  35. 35.
    Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Computational Intelligence 22(2), 73–99 (2006)MathSciNetCrossRefGoogle Scholar

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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