Sentic activation: A two-level affective common sense reasoning framework

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2012, Pages 186-192

  Cambria, Erik ^a   Olsher, Daniel ^a   Kwok, Kenneth ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

AI SYSTEMS; BACKGROUND KNOWLEDGE; COMMONSENSE KNOWLEDGE; COMMONSENSE REASONING; GENERALIZATION CAPABILITY; GRAPH MINING; HUMAN INTELLIGENCE; MULTIDIMENSIONALITY; REASONING FRAMEWORK; SENTIMENT ANALYSIS;

ARTIFICIAL INTELLIGENCE;

EID: 84868296994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Anderson, J., and Pirolli, P. 1984. Spread of activation. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:791-799.
2. Calvo, R., and D'Mello, S. 2010. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1(1):18-37.
3. Calvo, M., and Nummenmaa, L. 2007. Processing of unattended emotional visual scenes. Journal of Experimental Psychology: General 136:347-369.
4. Cambria, E., and Hussain, A. 2012. Sentic Computing: Techniques, Tools, and Applications. Berlin Heidelberg: Springer.
5. Cambria, E.; Mazzocco, T.; Hussain, A.; and Eckl, C. 2011. Sentic medoids: Organizing affective common sense knowledge in a multi-dimensional vector space. In Advances in Neural Networks, volume 6677 of Lecture Notes in Computer Science, 601-610. Berlin Heidelberg: Springer-Verlag.
6. Cambria, E.; Livingstone, A.; and Hussain, A. 2012. The hourglass of emotions. In Cognitive Behavioral Systems, Lecture Notes in Computer Science. Berlin Heidelberg: Springer.
7. Cambria, E.; Olsher, D.; and Kwok, K. 2012. Sentic panalogy: Swapping affective common sense reasoning strategies and foci. In CogSci.
8. Chaiken, S., and Trope, Y. 1999. Dual-Process Theories in Social Psychology. New York: Guilford.
9. Dalgleish, T.; Dunn, B.; and Mobbs, D. 2009. Affective neuroscience: Past, present, and future. Emotion Review 355-368.
10. Damasio, A. 2003. Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. Harcourt, Inc.
11. Duda, R., and Hart, P. 1973. Pattern Classification and Scene Analysis. New York: John Wiley & Sons Inc.
12. Eckart, C., and Young, G. 1936. The approximation of one matrix by another of lower rank. Psychometrika 1(3):211-218.
13. Epstein, S. 2003. Cognitive-experiential self-theory of personality. In Millon, T., and Lerner, M., eds., Comprehensive Handbook of Psychology, volume 5. Hoboken, NJ: Wiley & Sons. 159-184.
14. Fayyad, U.; Piatetsky, G.; and Smyth, P. 1996. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining. Cambridge: MIT Press. 1-36.
15. Garey, M., and Johnson, D. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman.
16. Hartigan, J., and Wong, M. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society 28(1):100-108.
17. Havasi, C.; Speer, R.; Pustejovsky, J.; and Lieberman, H. 2009. Digital intuition: Applying common sense using dimensionality reduction. IEEE Intelligent Systems 24(4):24-35.
18. Havasi, C.; Speer, R.; and Alonso, J. 2007. ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge. In RANLP.
19. Havasi, C.; Speer, R.; and Holmgren, J. 2010. Automated color selection using semantic knowledge. In AAAI CSK.
20. Hearst, M. 1997. Text data mining: Issues, techniques, and the relationship to information access. In UW/MS Workshop on Data Mining.
21. Kahneman, D. 2011. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
22. Kaufman, L., and Rousseeuw, P. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience.
23. Kirkpatrick, L., and Epstein, S. 1992. Cognitive experiential self-theory and subjective probability: Further evidence for two conceptual systems. Journal of Personality and Social Psychology 63:534-544.
24. Lieberman, M. 2007. Social cognitive neuroscience: A review of core processes. Annual Review of Psychology 58:259-89.
25. Ohman, A., and Soares, J. 1998. Emotional conditioning to masked stimuli: Expectancies for aversive outcomes following nonre- cognized fear-relevant stimuli. Journal of Experimental Psychology: General 127(1):69-82.
26. Pang, B.; Lee, L.; and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In EMNLP, 79-86.
27. Park, H., and Jun, C. 2009. A simple and fast algorithm for k-medoids clustering. Expert Systems with Applications 36(2):3336-3341.
28. Plutchik, R. 2001. The nature of emotions. American Scientist 89(4):344-350.
29. Scherer, K.; Shorr, A.; and Johnstone, T. 2001. Appraisal Processes in Emotion: Theory, Methods, Research. Canary: Oxford University Press.
30. Simoudis, E. 1996. Reality check for data mining. IEEE Expert 11(5).
31. Smith, E., and DeCoster, J. 2000. Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychological Review 4(2):108-131.
32. Strapparava, C., and Valitutti, A. 2004. WordNet-Affect: An affective extension of WordNet. In LREC.
33. Wall, M.; Rechtsteiner, A.; and Rocha, L. 2003. Singular value decomposition and principal component analysis. In Berrar, D.; Dubitzky, W.; and Granzow, M., eds., A Practical Approach to Microarray Data Analysis. Springer. 91-109.
34. Westen, D. 2002. Implications of developments in cognitive neuroscience for psychoanalytic psychotherapy. Harv Rev Psychiatry 10(6):369-73.
35. Wu, H.; Luk, R.; Wong, K.; and Kwok, K. 2008. Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems 26(3).