Switching Between Different Ways to Think

Multiple Approaches to Affective Common Sense Reasoning
  • Erik Cambria
  • Thomas Mazzocco
  • Amir Hussain
  • Tariq Durrani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6800)

Abstract

Emotions are different Ways to Think that our mind triggers to deal with different situations we face in our lives. Our ability to reason and make decisions, in fact, is strictly dependent on both our common sense knowledge about the world and our inner emotional states. This capability, which we call affective common sense reasoning, is a fundamental component in human experience, cognition, perception, learning and communication. For this reason, we cannot prescind from emotions in the development of intelligent user interfaces: if we want computers to be really intelligent, not just have the veneer of intelligence, we need to give them the ability to recognize, understand and express emotions. In this work, we argue how graph mining, multi-dimensionality reduction, clustering and space transformation techniques can be used on an affective common sense knowledge base to emulate the process of switching between different perspectives and finding novel ways to look at things.

Keywords

Sentic Computing AI Semantic Web NLP Cognitive and Affective Modeling Opinion Mining and Sentiment Analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vesterinen, E.: Affective Computing. In: Digital Media Research Seminar, Helsinki (2001)Google Scholar
  2. 2.
    Pantic, M.: Affective Computing. In: Encyclopedia of Multimedia Technology and Networking, vol. 1, pp. 8–14. Idea Group Reference, USA (2005)CrossRefGoogle Scholar
  3. 3.
    Elliott, C.D.: The Affective Reasoner: A Process Model of Emotions in a Multi-Agent System. PhD thesis, Northwestern University, Evanston (1992)Google Scholar
  4. 4.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation 39(2), 165–210 (2005)CrossRefGoogle Scholar
  5. 5.
    Kim, S., Hovy, E.: Automatic Detection of Opinion Bearing Words and Sentences. In: Proceedings of IJCNLP, Jeju Island, South Korea (2005)Google Scholar
  6. 6.
    Somasundaran, S., Wiebe, J., Ruppenhofer, J.: Discourse Level Opinion Interpretation. In: Proceedings of COLING, Manchester, UK (2008)Google Scholar
  7. 7.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of HLT/EMNLP, Vancouver, CA (2005)Google Scholar
  8. 8.
    Hu, M., Liu, B., Mcguinness, D., Ferguson, G.: Mining Opinion Features in Customer Reviews. In: Proceedings of AAAI, San Jose, USA (2004)Google Scholar
  9. 9.
    Goertzel, B., Silverman, K., Hartley, C., Bugaj, S., Ross, M.: The Baby Webmind project. In: Proceedings of AISB, Birmingham, UK (2000)Google Scholar
  10. 10.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: Sentic Computing: Exploitation of Common Sense for the Development of Emotion-Sensitive Systems. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) Development of Multimodal Interfaces, COST Seminar 2009. LNCS, vol. 5967, pp. 148–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Cambria, E., Grassi, M., Hussain, A., Havasi, C.: Sentic Computing for Social Media Marketing. Multimedia Tools and Application (2011), doi:10.1007/s11042-011-0815-0Google Scholar
  12. 12.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: Common Sense Computing: from the Society of Mind to Digital Intuition and Beyond. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) BioID MultiComm2009. LNCS, vol. 5707, pp. 252–259. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Ekman, P., Dalgleish, T., Power, M.: Handbook of Cognition and Emotion. Wiley, Chichester (1999)Google Scholar
  14. 14.
    Kapoor, A., Burleson, W., Picard, R.: Automatic Prediction of Frustration. International Journal of Human-Computer Studies 65, 724–736 (2007)CrossRefGoogle Scholar
  15. 15.
    Castellano, G., Kessous, L., Caridakis, G.: Multimodal Emotion Recognition from Expressive Faces, Body Gestures and Speech. In: Doctoral Consortium of ACII, Lisbon, Portugal (2007)Google Scholar
  16. 16.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: SenticSpace: Visualizing Opinions and Sentiments in a Multi-Dimensional Vector Space. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6279, pp. 385–393. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Minsky, M.: The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster, New York (2006)Google Scholar
  18. 18.
    Plutchik, R.: The Nature of Emotions. American Scientist 89(4), 344–350 (2001)CrossRefGoogle Scholar
  19. 19.
    Havasi, C., Speer, R., Alonso, J.: ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. In: Proceedings of RANLP, Borovets (2007)Google Scholar
  20. 20.
    Strapparava, C., Valitutti, A.: WordNet-Affect: an Affective Extension of WordNet. In: Proceedings of LREC, Lisbon, Portugal (2004)Google Scholar
  21. 21.
    Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)MATHGoogle Scholar
  22. 22.
    Havasi, C., Speer, R., Pustejovsky, J., Lieberman, H.: Digital Intuition: Applying Common Sense Using Dimensionality Reduction. IEEE Intelligent Systems 24, 24–35 (2009)CrossRefGoogle Scholar
  23. 23.
    Havasi, C., Speer, R., Holmgren, J.: Automated Color Selection Using Semantic Knowledge. In: Proceedings of AAAI CSK, Arlington, USA (2010)Google Scholar
  24. 24.
    Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., Munro, J.: Sentic Computing for Patient Centered Applications. In: Proceedings of IEEE ICSP, Beijing, China (2010)Google Scholar
  25. 25.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: AffectiveSpace: Blending Common Sense and Affective Knowledge to Perform Emotive Reasoning. In: WOMSA at CAEPIA, Seville, Spain (2009)Google Scholar
  26. 26.
    Eckart, C., Young, G.: The Approximation of One Matrix by Another of Lower Rank. Psychometrika 1(3), 211–218 (1936)CrossRefMATHGoogle Scholar
  27. 27.
    Cambria, E., Mazzocco, T., Hussain, A., Eckl, C.: Sentic Medoids: Organizing Affective Common Sense Knowledge in a Multi-Dimensional Vector Space. In: Liu, D. (ed.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 601–610. Springer, Heidelberg (2011)Google Scholar
  28. 28.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics). Wiley Interscience, Hoboken (2005)Google Scholar
  29. 29.
    Hartigan, J., Wong, M.: Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society 28(1), 100–108 (1979)MATHGoogle Scholar
  30. 30.
    Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness (Series of Books in the Mathematical Sciences). W.H. Freeman, New York (1979)MATHGoogle Scholar
  31. 31.
    Park, H., Jun, C.: A Simple and Fast Algorithm for K-Medoids Clustering. Expert Systems with Applications 36(2), 3336–3341 (2009)CrossRefGoogle Scholar
  32. 32.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons Inc., Chichester (1973)MATHGoogle Scholar
  33. 33.
    Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: A Publicly Available Semantic Resource for Opinion Mining. In: Proceedings of AAAI CSK, Arlington, USA (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Erik Cambria
    • 1
  • Thomas Mazzocco
    • 2
  • Amir Hussain
    • 2
  • Tariq Durrani
    • 2
  1. 1.National University of SingaporeSingapore
  2. 2.University of StirlingUnited Kingdom

Personalised recommendations