Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151519
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dc.contributor.authorChaturvedi, Itien_US
dc.contributor.authorSatapathy, Ranjanen_US
dc.contributor.authorCavallari, Sandroen_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2021-06-29T07:48:14Z-
dc.date.available2021-06-29T07:48:14Z-
dc.date.issued2019-
dc.identifier.citationChaturvedi, I., Satapathy, R., Cavallari, S. & Cambria, E. (2019). Fuzzy commonsense reasoning for multimodal sentiment analysis. Pattern Recognition Letters, 125, 264-270. https://dx.doi.org/10.1016/j.patrec.2019.04.024en_US
dc.identifier.issn0167-8655en_US
dc.identifier.other0000-0003-4602-2080-
dc.identifier.urihttps://hdl.handle.net/10356/151519-
dc.description.abstractThe majority of user-generated content posted online is in the form of text, images and videos but also physiological signals in games. AffectiveSpace is a vector space of affective commonsense available for English text but not for other languages nor other modalities such as electrocardiogram signals. We overcome this limitation by using deep learning to extract features from each modality and then projecting them to a common AffectiveSpace that has been clustered into different emotions. Because, in the real world, individuals tend to have partial or mixed sentiments about an opinion target, we use a fuzzy logic classifier to predict the degree of a particular emotion in AffectiveSpace. The combined model of deep convolutional neural networks and fuzzy logic is termed Convolutional Fuzzy Sentiment Classifier. Lastly, because the computational complexity of a fuzzy classifier is exponential with respect to the number of features, we project features to a four dimensional emotion space in order to speed up the classification performance.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation.ispartofPattern Recognition Lettersen_US
dc.rights© 2019 Published by Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleFuzzy commonsense reasoning for multimodal sentiment analysisen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.patrec.2019.04.024-
dc.identifier.scopus2-s2.0-85065463296-
dc.identifier.volume125en_US
dc.identifier.spage264en_US
dc.identifier.epage270en_US
dc.subject.keywordsSentiment Predictionen_US
dc.subject.keywordsFuzzy Logicen_US
dc.description.acknowledgementThis work is partially supported by the Data Science and Artificial Intelligence Center (DSAIR) at the Nanyang Technological University.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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