Yes / Aggressive 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18300 |
| Date | 10 January 2021 |
| Creators | Kumari, K., Singh, J.P., Dwivedi, Y.K., Rana, Nripendra P. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted manuscript |
| 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/) |
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