| Title |
Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification |
| ID_Doc |
44807 |
| Authors |
Yuvaraj, N; Chang, V; Gobinathan, B; Pinagapani, A; Kannan, S; Dhiman, G; Rajan, AR |
| Title |
Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.compeleceng.2021.107186 |
| 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. |
| Author Keywords |
Smart city; Cyberbullying detection; Deep neural network; Decision trees; Artificial intelligence |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000663706800001 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
https://publications.aston.ac.uk/id/eprint/43884/1/CAEE_final_manuscript_submisison_ver02E.pdf
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