Detecting Implicit Emotion Expressions from Text Using Ontological Resources and Lexical Learning

Abstract: 

In the past years, there has been a growing interest in developing computational methods for affect detection from text. Although much research has been done in the field, this task still remains far from being solved, as the presence of affect is only in a very small number of cases marked by the presence of emotion-related words. In the rest of the cases, no such lexical clues of emotion are present in text and special commonsense knowledge is necessary in order to interpret the meaning of the situation described and understand its affective connotations. In the light of the challenges posed by the detection of emotions from contexts in which no lexical clue is present, we proposed and implemented a knowledge base ? EmotiNet ? that stores situations in which specific emotions are felt, represented as ?action chains?. Following the initial evaluations, in this chapter, we describe and evaluate two different methods to extend the knowledge contained in EmotiNet: using lexical and ontological knowledge. Results show that such types of knowledge sources are complementary and can help to improve both the precision, as well as the recall of implicit emotion detection systems based on commonsense knowledge.

Authors
Authors: 
TANEV Hristo, BALAHUR DOBRESCU Alexandra, HERMIDA CARBONELL Jesus Maria
Publication Year
Publication Year: 
2013
Type

Type:

Appears in Collections
Appears in Collections: 
Institute for the Protection and Security of the Citizen
Science Areas
Keywords
JRC Institutes
Publisher
Publisher: 
Springer Berlin Heidelberg
ISBN
ISBN: 
978-3-642-31781-1 (print),978-3-642-31782-8 (online)
ISSN
ISSN: 
2192-032X
Citation
Citation: 
New Trends of Research in Ontologies and Lexical Resources Ideas, Projects, Systems, p. 235-255