Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16515
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: Sentic Avatar: Multimodal Affective Conversational Agent with Common Sense
Authors: Cambria, Erik
Hupont, Isabelle
Hussain, Amir
Cerezo, Eva
Baldassarri, Sandra
Contact Email: ahu@cs.stir.ac.uk
Editors: Esposito, A
Esposito, AM
Martone, R
Muller, VC
Scarpetta, G
Citation: Cambria E, Hupont I, Hussain A, Cerezo E & Baldassarri S (2011) Sentic Avatar: Multimodal Affective Conversational Agent with Common Sense. In: Esposito A, Esposito AM, Martone R, Muller VC, Scarpetta G (ed.). Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues: Third COST 2102 International Training School, Caserta, Italy, March 15-19, 2010, Revised Selected Papers. Lecture Notes in Computer Science, 6456, Berlin Heidelberg: Springer, pp. 81-95.
Keywords: AI
Sentic Computing
NLP
Facial Expression Analysis
Sentiment Analysis
Multimodal Affective HCI
Conversational Agents
Issue Date: 2011
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science, 6456
Abstract: The capability of perceiving and expressing emotions through different modalities is a key issue for the enhancement of human-computer interaction. In this paper we present a novel architecture for the development of intelligent multimodal affective interfaces. It is based on the integration of Sentic Computing, a new opinion mining and sentiment analysis paradigm based on AI and Semantic Web techniques, with a facial emotional classifier and Maxine, a powerful multimodal animation engine for managing virtual agents and 3D scenarios. One of the main distinguishing features of the system is that it does not simply perform emotional classification in terms of a set of discrete emotional lables but it operates in a continuous 2D emotional space, enabling the integration of the different affective extraction modules in a simple and scalable way.
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Type: Part of book or chapter of book
URI: http://hdl.handle.net/1893/16515
URL: http://link.springer.com/chapter/10.1007%2F978-3-642-18184-9_8
Affiliation: University of Stirling
Aragon Institute of Technology
Computing Science - CSM Dept
University of Zaragoza
University of Zaragoza

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