Skip to main content
SpringerLink
Log in

Sentic Web: A New Paradigm for Managing Social Media Affective Information

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The recent success of media-sharing services caused an exponential growth of community-contributed multimedia data on the Web and hence a consistent shift of the flow of information from traditional communication channels to social media ones. Retrieving relevant information from this kind of data is getting more and more difficult, not only for their volume, but also for the different nature and formats of their contents. In this work, we introduce Sentic Web, a new paradigm for the management of social media affective information, which exploits AI and Semantic Web techniques to extract, encode, and represent opinions and sentiments over the Web. In particular, the computational layer consists in an intelligent engine for the inference of emotions from text, the representation layer is developed on the base of specific domain ontologies, and the application layer is based on the faceted browsing paradigm to make contents available as an interconnected knowledge base.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Hendler J, Shadbolt N, Hall W, Berners-Lee T, Weitzner D. Web science: an interdisciplinary approach to understanding the world wide web. Comm ACM 2008;51(7):60–69.

    Article  Google Scholar 

  2. Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. LNCS, vol. 5967. Berlin: Springer; 2010. pp. 148–156.

  3. Grassi M. Developing HEO human emotion ontology. LNCS, vol. 5707. Berlin: Springer; 2009. pp. 244–251.

  4. Umbria – http://umbrialistens.co.

  5. Cymfony – http://www.cymfony.co.

  6. Nielsen – http://nielsen-online.co.

  7. Evolve24 – http://evolve24.co.

  8. Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: EMNLP, Philadelphia 2002.

  9. Sood S, Owsley S, Hammond K, Birnbaum L. Reasoning through search: a novel approach to sentiment classification. Northwestern University Tech Report Number NWU-EECS-07-05 2007.

  10. Turney P. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: ACL, Philadelphia 2002.

  11. Alm C, Roth D, Sproat R. Emotions from text: machine learning for text-based emotion prediction. In: HLT/EMNLP, Vancouver 2005.

  12. Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, Barcelona 2004.

  13. Sood S, Vasserman L. Esse: exploring mood on the web. In: ICWSM, Seattle 2009.

  14. Antoniou G, Van Harmelen F. A semantic web primer. Cambridge: Mit Press; 2004.

    Google Scholar 

  15. Cambria E, Hussain A, Havasi C, Eckl C. Common sense computing: from the society of mind to digital intuition and beyond. LNCS, vol. 5707. Berlin: Springer 2009. pp. 252–259.

  16. Cambria E, Hussain A, Havasi C, Eckl C. AffectiveSpace: blending common sense and affective knowledge to perform emotive reasoning. In: WOMSA at CAEPIA, Seville 2009.

  17. Havasi C, Speer R, Alonso J. ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In: RANLP, Borovets 2007.

  18. Strapparava C, Valitutti A. WordNet-Affect: an affective extension of WordNet. In: LREC, Lisbon 2004.

  19. Cambria E, Hussain A, Havasi C, Eckl C. SenticSpace: visualizing opinions and sentiments in a multi-dimensional vector space. LNAI, vol. 6279. Berlin: Springer; 2010. pp. 385–393.

  20. Plutchik R. The nature of emotions. Am Scient 2001;89(4):344–350.

    Google Scholar 

  21. Minsky M: The emotion machine. New York: Simon and Schuster; 2006.

    Google Scholar 

  22. The Humaine Project – http://emotion-research.ne.

  23. COST Action 2102: Cross-Modal Analysis of Verbal and Non-Verbal Communication – http://cost2102.cs.stir.ac.u.

  24. W3C Emotion Markup Language – http://w3.org/2005/Incubator/emotio.

  25. Ontology for Media Resource – http://w3.org/TR/mediaont-1.

  26. Friend of a Friend Project – http://foaf-project.or.

  27. Sentic Web demo – http://semedia.dibet.univpm.it/senticwe.

  28. Cheng X, Dale C, Liu J. Statistics and Social Network of YouTube Videos. In: IEEE IWQoS, Enschede 2008.

  29. SIMILE Exhibit API – http://simile-widgets.org/exhibi.

  30. Sesame – http://www.openrdf.or.

  31. LiveJournal – http://livejournal.co.

Download references

Acknowledgments

This work was undertaken during the first author’s research visit to Sitekit Labs and partly funded with the aid of a COST 2102 Short Term Scientific Mission.

Author information

Authors and Affiliations

  1. Department of Biomedical, Electronic and Telecommunication Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy

    Marco Grassi & Francesco Piazza

  2. Department of Computing Science and Mathematics, University of Stirling, Stirling, FK94LA, UK

    Erik Cambria & Amir Hussain

Authors
  1. Marco Grassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cambria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesco Piazza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Grassi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Grassi, M., Cambria, E., Hussain, A. et al. Sentic Web: A New Paradigm for Managing Social Media Affective Information. Cogn Comput 3, 480–489 (2011). https://doi.org/10.1007/s12559-011-9101-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-011-9101-8

Keywords

Search

Navigation