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dc.contributor.authorKumari, K.
dc.contributor.authorSingh, J.P.
dc.contributor.authorDwivedi, Y.K.
dc.contributor.authorRana, Nripendra P.
dc.date.accessioned2021-01-10T18:45:41Z
dc.date.accessioned2021-01-12T14:00:53Z
dc.date.available2021-01-10T18:45:41Z
dc.date.available2021-01-12T14:00:53Z
dc.date.issued2021
dc.identifier.citationKumari K, Singh JP, Dwivedi YK et al (2021) Multi-modal Aggression Identification Using Convolutional Neural Network and Binary Particle Swarm Optimization. Future Generation Computer Systems. Accepted for publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18300
dc.descriptionYesen_US
dc.description.abstractAggressive posts containing symbolic and offensive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features.en_US
dc.language.isoenen_US
dc.rights© 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.subjectCyber-aggressionen_US
dc.subjectCyberbullyingen_US
dc.subjectMulti-modal dataen_US
dc.subjectConvolutional neural networken_US
dc.subjectBinary particle swarm optimisationen_US
dc.titleMulti-modal Aggression Identification Using Convolutional Neural Network and Binary Particle Swarm Optimizationen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-01-10
dc.typeArticleen_US
dc.date.EndofEmbargo2022
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo, 12 months from first publication.en_US
dc.date.updated2021-01-10T18:45:43Z
refterms.dateFOA2021-01-12T14:01:16Z
dc.openaccess.statusGreenen_US


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