CYBERBULLYING DETECTION USING NLP

Abstract: Cyberbullying detection using Natural Language Processing (NLP) aims to identify harmful or abusive content on online platforms. This research focuses on classifying text data into cyberbullying and non-cyberbullying categories using advanced NLP and machine learning models. The dataset includes a variety of online comments, which are cleaned, tokenized, and vectorized using TF-IDF techniques. Machine learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machine are evaluated for performance. Results show that ensemble-based methods outperform simple classifiers, achieving high accuracy and precision in detecting cyberbullying content.

Keywords: NLP, Cyberbullying Detection, Text Classification, Machine Learning, Sentiment Analysis