Paper Info
Title
EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories
Abstract
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.
Year
DOI
Venue
2011
10.1007/978-3-642-22327-3_4
NLDB
Keywords
Field
DocType
affective consequence,automatic detection,emotion-triggering concept,commonsense knowledge,artificial intelligence,self-reported affective situation,appraisal theory,commonsense affective meaning,difficult task,natural language processing,emotinet knowledge base,emotion detection,knowledge base
Commonsense knowledge,Cognitive science,Computer science,Emotion detection,Natural language processing,Artificial intelligence,Knowledge base,Affect (psychology),Valuation (finance),Semantics
Conference
Volume
ISSN
Citations 
6716
0302-9743
10
PageRank 
References 
Authors
0.60
18
4
Name
Order
Citations
PageRank
Alexandra Balahur159340.19
Jesús M. Hermida21007.89
Andrés Montoyo367867.78
Rafael Muñoz4273.22