DocumentCode :
243564
Title :
A Hybrid Approach for Emotion Detection in Support of Affective Interaction
Author :
Gievska, Sonja ; Koroveshovski, Kiril ; Chavdarova, Tatjana
Author_Institution :
Dept. of Comput. Sci., George Washington Univ., Washington, DC, USA
fYear :
2014
fDate :
14-14 Dec. 2014
Firstpage :
352
Lastpage :
359
Abstract :
Affective interaction is a new emerging area of interest for interaction designers. This research explores the potential of our hybrid approach that relies on both, lexical and machine learning techniques for detection of Ekman´s six emotional categories in user´s text. The initial results of the performance evaluation of the proposed hybrid approach are encouraging and comparable to related research. A demonstrative mobile application that employs the proposed approach was developed to engage the users in a dialogue that solicits their reflections on various daily events and provides appropriate affective responses.
Keywords :
behavioural sciences computing; emotion recognition; learning (artificial intelligence); Ekman six emotional categories; affective interaction; affective responses; daily events reflections; emotion detection; hybrid approach; interaction designers; lexical techniques; machine learning techniques; mobile application; user text; Context; Learning systems; Measurement; Mobile communication; Performance analysis; Pragmatics; Support vector machines; emotion detection; lexical analysis; mobile affective interaction; valence shifting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
Type :
conf
DOI :
10.1109/ICDMW.2014.130
Filename :
7022618
Link To Document :
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