Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification


Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier.cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification engine that initially pre-processes the tweets, eliminates noise and other background information, extracts the selected features and classifies without data overfitting. The study develops a novel Deep Decision Tree classifier that utilizes the hidden layers of Deep Neural Network (DNN) as its tree node to process the input elements. The validation confirms the accuracy of classification using the novel Deep classifier with its improved text classification accuracy.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Funding Information: This work is partly supported by VC Research (VCR 0000061) for Prof Chang.
Uncontrolled Keywords: Artificial intelligence,Cyberbullying detection,Decision trees,Deep neural network,Smart city,Control and Systems Engineering,Computer Science(all),Electrical and Electronic Engineering
Publication ISSN: 0045-7906
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 1877?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2021-06-01
Published Online Date: 2021-05-07
Accepted Date: 2021-04-23
Authors: Yuvaraj, Natarajan
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Gobinathan, Balasubramanian
Pinagapani, Arulprakash
Kannan, Srihari
Dhiman, Gaurav
Rajan, Arsath Raja

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