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

Natarajan Yuvaraj, Victor Chang, Balasubramanian Gobinathan, Arulprakash Pinagapani, Srihari Kannan, Gaurav Dhiman, Arsath Raja Rajan

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number107186
JournalComputers and Electrical Engineering
Volume92
DOIs
Publication statusPublished - 1 Jun 2021

Bibliographical note

Funding Information:
This work is partly supported by VC Research (VCR 0000061) for Prof Chang.

Publisher Copyright:
© 2021

Fingerprint

Dive into the research topics of 'Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification'. Together they form a unique fingerprint.

Cite this