EmotiNet: A Knowledge Base for Emotion Detection in Text Built on the Appraisal Theories

The automatic detection of emotions is a difficult task in Artificial Intelligence. In the field of Natural Language Processing, the challenge of automatically detecting emotion from text has been tackled from many perspectives. Nonetheless, the majority of the approaches contemplated only the word level. Due to the fact that emotion is most of the times not expressed through specific words, but by evoking situations that have a commonsense affective meaning, the performance of existing systems is low. This article presents the EmotiNet knowledge base - a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. The core of the resource is built from a set of self-reported affective situations and extended with external sources of commonsense knowledge on emotion-triggering concepts. The results of the preliminary evaluations show that the approach is appropriate for capturing and storing the structure and the semantics of real situations and predict the emotional responses triggered by actions presented in text.

[1]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.

[2]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[3]  Patrick Pantel,et al.  Automatically Labeling Semantic Classes , 2004, NAACL.

[4]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss analysis , 2005, CIKM 2005.

[5]  M. Dyer.  Emotions and their computations: Three computer models , 1987 .

[6]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[7]  Marco Grassi.  Developing HEO Human Emotions Ontology , 2009, COST 2101/2102 Conference.

[8]  Chu-Ren Huang,et al.  Cause Event Representations for Happiness and Surprise , 2009, PACLIC.

[9]  Hugo Liu,et al.  ConceptNet — A Practical Commonsense Reasoning Tool-Kit , 2004 .

[10]  Pero Subasic,et al.  Affect analysis of text using fuzzy semantic typing , 2001, IEEE Trans. Fuzzy Syst..

[11]  Henry Lieberman,et al.  A model of textual affect sensing using real-world knowledge , 2003, IUI '03.

[12]  K. Scherer.  Studying the emotion-antecedent appraisal process: An expert system approach , 1993 .

[13]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[14]  Paloma Moreda,et al.  Corpus-based semantic role approach in information retrieval , 2007, Data Knowl. Eng..

[15]  Anna Esposito,et al.  Biometric ID Management and Multimodal Communication, Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid, Spain, September 16-18, 2009. Proceedings , 2009, COST 2101/2102 Conference.

[16]  Bruno Pouliquen,et al.  Opinion Mining on Newspaper Quotations , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[17]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[18]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[19]  Carlo Strapparava,et al.  SemEval-2007 Task 14: Affective Text , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[20]  Andrés Montoyo,et al.  Applying a culture dependent emotion triggers database for text valence and emotion classification , 2008, Proces. del Leng. Natural.

[21]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[22]  Rosalind W. Picard.  Affective Computing , 1997 .

[23]  Sergey Brin,et al.  Extracting Patterns and Relations from the World Wide Web , 1998, WebDB.

[24]  T. Dalgleish,et al.  Handbook of cognition and emotion , 1999 .

[25]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[26]  Andrew Ortony,et al.  The Cognitive Structure of Emotions , 1988 .

[27]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[28]  Luis Gravano,et al.  Extracting Relations from Large Plain-Text Collections , 1999 .

[29]  Jeannett Martin,et al.  The Language of Evaluation: Appraisal in English , 2005 .

[30]  J. Rivera.  A structural theory of the emotions , 1977 .

[31]  Luis Gravano,et al.  Snowball: extracting relations from large plain-text collections , 2000, DL '00.

[32]  R. Plutchik.  The Nature of Emotions , 2001 .

[33]  P. Johnson-Laird,et al.  The language of emotions: An analysis of a semantic field , 2013 .

[34]  Eugene Charniak,et al.  Finding Parts in Very Large Corpora , 1999, ACL.

[35]  Patrick Pantel,et al.  VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations , 2004, EMNLP.