Publication Date: 2023/05/13
Abstract: Cyber bullying is a serious issue that affects individuals of all ages, particularly children and teenagers who are more vulnerable to online harassment. With the growing use of social media and other online platforms, it has become increasingly important to develop effective methods to detect and prevent cyber bullying. In this project, we propose a machine learningbased approach for cyber bullying detection. The proposed system uses natural language processing (NLP) techniques to analyse text messages and identify patterns of abusive and aggressive behaviour. We apply various classification algorithms, such as Logistic Regression, Decision Trees Classifier and Gaussian Naïve bayes, to train our model and evaluate its performance. We also explore the use of ensemble methods, such as Random Forest classifier and adaboost classifier, to improve the accuracy of our model. We use publicly available datasets to test our system and compare its performance with other existing approaches. Our results show that the proposed machine literacy- grounded approach can effectively identify cyber bullying with high delicacy, perceptivity, and particularity. This project has significant implications for the development of automated systems that can help protect individuals from online harassment and promote a safer and more inclusive online environment
Keywords: Cyberbullying, Harassment, Machine Learning, Natural Language Processing, social media analysis, Text classification, Logistic Regression, Decision Tree Classifier, Gaussian Naïve Bayes, Ensemble Methods, Adaboost classifier, Random Forest Classifier, Sentiment analysis and Behavioural analysis
DOI: https://doi.org/10.5281/zenodo.7937266
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23APR2306.pdf
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