Detecting Cyberbullying Behavior in Cyber Data using Bagging Classifier and comparing its Capability over Support Vector Machine Algorithm
- Resource Type
- Conference
- Authors
- Manikanta, P. Pavan V S N; Bhavani, R.; Anbazhagan, K.
- Source
- 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) Science Technology Engineering and Mathematics (ICONSTEM), 2023 Eighth International Conference on. :1-5 Apr, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Blogs
Cyberbullying
Support vector machine classification
Machine learning
Mathematics
Classification algorithms
- Language
Aim: The suggested research will attempt to perform novel cyberbullying detection by classifying between offensive and non-offensive tweets from Twitter and from its dataset using Bagging Classifier and Support Vector Machine. Materials and Methods: A dataset of abusive and non-offensive tweets is used to develop the Bagging Classifier. Bagging Classifier uses and develops a machine learning method to identify tweets as offensive or not. The sample size was calculated to be 40 per set, and the quality was verified and recorded using Gpower of 80%. Results: The accuracy was maximum in classifications of offensive and non-offensive tweets using Bagging Classifier (94.2%) with a minimum mean error and compared to Support Vector Machine (89.4%). The differences between the classifiers are statistically negligible (p=0.44). Conclusion: In the classification of offensive and non-offensive tweets, the study shows that the Bagging Classifier algorithm outperforms the SVM approach.