Please use this identifier to cite or link to this item: https://doi.org/10.1109/CIHLI.2013.6613272
Title: Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics
Authors: Cambria, E. 
Howard, N.
Hsu, J.
Hussain, A.
Keywords: Affective common-sense
Emotion recognition
Facial expression analysis
Multimodal fusion
SenticNet
Issue Date: 2013
Citation: Cambria, E.,Howard, N.,Hsu, J.,Hussain, A. (2013). Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics. Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 : 108-117. ScholarBank@NUS Repository. https://doi.org/10.1109/CIHLI.2013.6613272
Abstract: The capability of interpreting the conceptual and affective information associated with natural language through different modalities is a key issue for the enhancement of human-agent interaction. The proposed methodology, termed sentic blending, enables the continuous interpretation of semantics and sentics (i.e., the conceptual and affective information associated with natural language) based on the integration of an affective common-sense knowledge base with any multimodal signal-processing module. In this work, in particular, sentic blending is interfaced with a facial emotional classifier and an opinion mining engine. One of the main distinguishing features of the proposed technique is that it does not simply perform cognitive and affective classification in terms of discrete labels, but it operates in a multidimensional space that enables the generation of a continuous stream characterising user's semantic and sentic progress over time, despite the outputs of the unimodal categorical modules have very different time-scales and output labels. © 2013 IEEE.
Source Title: Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/116779
ISBN: 9781467359238
DOI: 10.1109/CIHLI.2013.6613272
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