TY - GEN
T1 - Sentic blending
T2 - 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
AU - Cambria, Erik
AU - Howard, Newton
AU - Hsu, Jane
AU - Hussain, Amir
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Affective common-sense
KW - Emotion recognition
KW - Facial expression analysis
KW - Multimodal fusion
KW - SenticNet
UR - https://www.scopus.com/pages/publications/84886665707
U2 - 10.1109/CIHLI.2013.6613272
DO - 10.1109/CIHLI.2013.6613272
M3 - Conference contribution
AN - SCOPUS:84886665707
SN - 9781467359238
T3 - Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 108
EP - 117
BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -