SentiCircles: A Platform for Contextual and Conceptual Sentiment Analysis

  • Hassan Saif
  • Maxim Bashevoy
  • Steve Taylor
  • Miriam Fernandez
  • Harith Alani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics’ feelings towards policies, brands, business, etc. In this paper we present SentiCircles, a platform that captures feedback from social media conversations and applies contextual and conceptual sentiment analysis models to extract and summarise sentiment from these conversations. It provides a novel sentiment navigation design where contextual sentiment is captured and presented at term/entity level, enabling a better alignment of positive and negative sentiment to the nature of the public debate.

Keywords

Social media Sentiment analysis 

References

  1. 1.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of COLING, Beijing, China (2010)Google Scholar
  2. 2.
    Cambria, E.: An introduction to concept-level sentiment analysis. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part II. LNCS, vol. 8266, pp. 478–483. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the ICWSM, Barcelona, Spain (2011)Google Scholar
  4. 4.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: ICWSM, vol. 11, pp. 122–129 (2010)Google Scholar
  5. 5.
    Saif, H., Fernandez, M., He, Y., Alani, H.: SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 83–98. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Saif, H., He, Y., Fernandez, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manag. 52(1), 5–19 (2016). doi:10.1016/j.ipm.2015.01.005. ISSN: 0306–4573CrossRefGoogle Scholar
  8. 8.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hassan Saif
    • 1
  • Maxim Bashevoy
    • 2
  • Steve Taylor
    • 2
  • Miriam Fernandez
    • 1
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteOpen UniversityMilton KeynesUK
  2. 2.IT Innovation CentreUniversity of SouthamptonSouthamptonUK

Personalised recommendations