The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan.-March (2012 vol.3)
pp: 88-101
A. Balahur , Dept. de Lenguajes y Sist. Informaticos, Univ. de Alicante, Alicante, Spain
J. M. Hermida , Dept. de Lenguajes y Sist. Informaticos, Univ. de Alicante, Alicante, Spain
A. Montoyo , Dept. de Lenguajes y Sist. Informaticos, Univ. de Alicante, Alicante, Spain
ABSTRACT
The task of automatically detecting emotion in text is challenging. This is due to the fact that most of the times, textual expressions of affect are not direct-using emotion words-but result from the interpretation and assessment of the meaning of the concepts and interaction of concepts described in the text. This paper presents the core of EmotiNet, a new knowledge base (KB) for representing and storing affective reaction to real-life contexts, and the methodology employed in designing, populating, and evaluating it. The basis of the design process is given by a set of self-reported affective situations in the International Survey on Emotion Antecedents and Reactions (ISEAR) corpus. We cluster the examples and extract triples using Semantic Roles. We subsequently extend our model using other resources, such as VerbOcean, ConceptNet, and SentiWordNet, with the aim of generalizing the knowledge contained. Finally, we evaluate the approach using the representations of other examples in the ISEAR corpus. We conclude that EmotiNet, although limited by the domain and small quantity of knowledge it presently contains, represents a semantic resource appropriate for capturing and storing the structure and the semantics of real events and predicting the emotional responses triggered by chains of actions.
INDEX TERMS
text analysis, behavioural sciences computing, emotion recognition, knowledge based systems, semantic Web, semantic resource, EmotiNet, knowledge base, emotion detection, appraisal theory model, text, emotion words, affective reaction, real-life contexts, international survey on emotion antecedents and reactions corpus, semantic roles, VerbOcean, ConceptNet, SentiWordNet, Appraisal, Context, Psychology, Knowledge based systems, Computational modeling, Context modeling, affect sensing and analysis., Affective computing, emotion detection, emotion theory, knowledge and data engineering tools and techniques
CITATION
A. Balahur, J. M. Hermida, A. Montoyo, "Building and Exploiting EmotiNet, a Knowledge Base for Emotion Detection Based on the Appraisal Theory Model", IEEE Transactions on Affective Computing, vol.3, no. 1, pp. 88-101, Jan.-March 2012, doi:10.1109/T-AFFC.2011.33
REFERENCES
[1] K. Oatley, Emotions: A Brief History. Oxford: Wiley Blackwell, 2004.
[2] C. Ratner, “A Cultural-Psychological Analysis of Emotions,” Culture and Psychology, vol. 6, pp. 5-39, 2000.
[3] P. Goldie, Emotions: A Philosophical Exploration. Oxford Univ. Press, 2000.
[4] D. Evans, Emotions: The Science of Sentiment. Oxford Univ. Press, 2001.
[5] K. Oatley, D. Keltner, and J.M. Jenkins, Understanding Emotions, second ed. Wiley-Blackwell, 2006.
[6] R. Picard, “Affective Computing,” technical report, MIT Media Laboratory, 1995.
[7] R.A. Calvo and S. D'Mello, “Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications,” IEEE Trans. Affective Computing, vol. 1, no. 1, pp. 18-37, Jan.-June 2010.
[8] C. Strapparava and R. Mihalcea, “Semeval 2007 Task 14: Affective Text,” Proc. Fourth Int'l Workshop Semantic Evaluations, pp. 70-74, 2007.
[9] J.W. Pennebaker, M.R. Mehl, and K. Niederhoffer, “Psychological Aspects of Natural Language Use: Our Words, Our Selves,” Ann. Rev. of Psychology, vol. 54, pp. 547-577, 2003.
[10] A. Balahur and A. Montoyo, “Applying a Culture Dependent Emotion Triggers Database for Text Valence and Emotion Classification,” Proc. AISB Convention Comm., Interaction and Social Intelligence, 2008.
[11] A. Balahur and R. Steinberger, “Rethinking Opinion Mining in Newspaper Articles: from Theory to Practice and Back,” Proc. First Workshop Opinion Mining and Sentiment Analysis, pp. 1-12, 2009.
[12] K. Scherer, “Appraisal Theory,” Handbook of Cognition and Emotion, John Wiley & Sons Ltd., 1989.
[13] H. Liu and P. Singh, “ConceptNet: A Practical Commonsense Reasoning Toolkit,” BT Technology J., vol. 22, pp. 211-226, 2004.
[14] T. Chklovski and P. Pantel, “VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 33-40, 2004.
[15] K. Scherer and H. Wallbott, “The ISEAR Questionnaire and Codebook,” Geneva Emotion Research Group, 1997.
[16] K. Scherer, “Emotional Expression Is Subject to Social and Technological Change: Extrapolating to the Future,” Social Science Information, vol. 2, no. 40, pp. 125-151, 1987.
[17] K. Scherer, “Toward a Dynamic Theory of Emotion. The Component Process of Affective States,” Cognition and Emotion, vol. 1, no. 1, 2001.
[18] K. Scherer, “What Are Emotions? and How Can They Be Measured?” Social Science Information, vol. 3, no. 44, pp. 695-729, 2005.
[19] R.E. Thayer, Calm Energy: How People Regulate Mood With Food and Exercise. Oxford Univ. Press, 2001.
[20] M. Dyer, “Emotions and Their Computations: Three Computer Models,” Cognition and Emotion, vol. 1, pp. 323-347, 1987.
[21] P. Subasic and A. Huettner, “Affect Analysis of Text Using Fuzzy Semantic Typing,” IEEE Trans. Fuzzy Systems, vol. 9, no. 4, pp. 483-496, Aug. 2001.
[22] C. Strapparava and A. Valitutti, “WordNet Affect: An Affective Extension of WordNet,” Proc. Fourth Int'l Conf. Language Resources and Evaluation, pp. 1083-1086, 2004.
[23] A. Esuli and F. Sebastiani, “Determining the Semantic Orientation of Terms through Gloss Analysis,” Proc. 14th ACM Int'l Conf. Information and Knowledge Management, pp. 617-624, 2005.
[24] E. Riloff, J. Wiebe, and T. Wilson, “Learning Subjective Nouns Using Extraction Pattern Bootstrapping,” Proc. Conf. Natural Language Learning, pp. 25-32, 2003.
[25] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 79-86, 2002.
[26] J. Wiebe and E. Riloff, “Creating Subjective and Objective Sentence Classifiers from Unannotated Texts,” Proc. Sixth Int'l Conf. Computational Linguistics and Intelligent Text Processing, pp. 73-99, 2005.
[27] S.Y. Mei Lee, Y. Chen, and C.-R. Huang, “Cause Event Representations of Happiness and Surprise,” Proc. Pacific Asia Conf. Language, Information and Computation, 2009.
[28] H. Liu, H. Lieberman, and T. Selker, “A Model of Textual Affect Sensing Using Real-World Knowledge,” Proc. Eighth Int'l Conf. Intelligent User Interfaces, pp. 125-132, 2003.
[29] E. Cambria, A. Hussain, C. Havasi, and C. Eckl, “Affective Space: Blending Common Sense and Affective Knowledge to Perform Emotive Reasoning,” Proc. First Workshop Opinion Mining and Sentiment Analysis, pp. 32-41, 2009.
[30] T. Danisman and A. Alpkocak, “Feeler: Emotion Classification of Text Using Vector Space Model,” Proc. AISB Convention, Comm., Interaction and Social Intelligence, 2008.
[31] J. De Rivera, “A Structural Theory of the Emotions,” Psychological Issues, vol. 10, no. 4, 1977.
[32] N. Frijda, The Emotions. Cambridge Univ. Press, 1986.
[33] A. Ortony, G.L. Clore, and A. Collins, The Cognitive Structure of Emotions. Cambridge Univ. Press, 1988.
[34] P.N. Johnson-Laird and K. Oatley, “The Language of Emotions: An Analysis of a Semantic Field,” Cognition and Emotion, vol. 3, pp. 81-123, 1989.
[35] R.S. Lazarus and C.A. Smith, “Knowledge and Appraisal in the Cognition-Emotion Relationship,” Cognition and Emotion, vol. 2, pp. 281-300, 1988.
[36] K.R. Scherer, “Studying the Emotion-Antecedent Appraisal Process: An Expert System Approach,” Cognition and Emotion, vol. 7, nos. 3/4, pp. 323-355, 1993.
[37] J.R. Martin and P.R. White, Language of Evaluation: Appraisal in English. Palgrave Macmillan, 2005.
[38] M.A.K. Halliday, An Introduction to Functional Grammar. Edward Ar nold, 1994.
[39] C. Fellbaum, WordNet: An Electronic Lexical Database. MIT Press, 1998.
[40] F. Suchanek, G. Kasnei, and G. Weikum, “YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia,” Proc. Int'l World Wide Web Conf., pp. 697-706, 2007.
[41] P. Pantel and D. Ravichandran, “Automatically Labeling Semantic Classes,” Proc. Human Language Technology/North Am. Assoc. for Computational Linguistics, pp. 321-328, 2004.
[42] M. Berland and E. Charniak, “Finding Parts in Very Large Corpora,” Proc. 37th Ann. Meeting Assoc. for Computational Linguistics on Computational Linguistics, pp. 57-64, 1999.
[43] S. Brin, “Extracting Patterns and Relations from the World-Wide Web,” Proc. Int'l Workshop Web and Databases, pp. 172-183, 1998.
[44] E. Agichtein and L. Gravano, “Snowball: Extracting Relations from Large Plain-Text Collections,” Proc. Fifth ACM Int'l Conf. Digital Libraries, pp. 85-94, 2000.
[45] R. Studer, R.V. Benjamins, and D. Fensel, “Knowledge Engineering: Principles and Methods,” Data and Knowledge Eng., vol. 25, nos. 1/2, pp. 161-197, 1998.
[46] C.E. Izard, The Face of Emotion. Appleton-Century-Crofts, 1971.
[47] P. Ekman, “Are There Basic Emotions?” Psychology Rev., vol. 99, no. 3, pp. 550-553, July 1992.
[48] R. Plutchik, “The Nature of Emotions,” Am. Scientist, vol. 89, p. 344, 2001.
[49] W. Parrott, Emotions in Social Psychology. Psychology Press, 2001.
[50] P. Moreda, B. Navarro, and M. Palomar, “Corpus-Based Semantic Role Approach in Information Retrieval,” Data and Knowledge Eng., vol. 61, no. 3, pp. 467-483, 2007.
[51] H. Schmid, “Probabilistic Part-of-Speech Tagging Using Decision Trees,” Proc. Int'l Conf. New Methods in Language Processing, pp. 44-49, 1994.
[52] M. Lesk, “Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone,” Proc. Fifth Ann. Int'l Conf. Systems Documentation, pp. 24-26, 1986.
[53] M. Grüninger and M.S. Fox, “Formal Ontology in Information Systems,” Proc. IJCAI Workshop Basic Ontological Issues in Knowledge Sharing, pp. 3-15, 1995.
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool