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Title: Fuzzy commonsense reasoning for multimodal sentiment analysis
Authors: Chaturvedi, Iti
Satapathy, Ranjan
Cavallari, Sandro
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Chaturvedi, I., Satapathy, R., Cavallari, S. & Cambria, E. (2019). Fuzzy commonsense reasoning for multimodal sentiment analysis. Pattern Recognition Letters, 125, 264-270.
Journal: Pattern Recognition Letters
Abstract: The 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.
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2019.04.024
Rights: © 2019 Published by Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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