Authors :
Kundharapu Vasudeva; Bestha Raghavendra Raj Kiran; Shaik Vaseem Akram; Bandaru Vijaya Prakash
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3O7oeYS
DOI :
https://doi.org/10.5281/zenodo.7937266
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
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
