A Context-Aware Cyberbullying Detection Model Using TF-IDF and NLP Techniques

Description:

Cyberbullying has become a serious problem on social media platforms, causing emotional and psychological harm to individuals. Manual monitoring of online content is time-consuming and inefficient due to the large volume of user-generated text. This project proposes a context-aware cyberbullying detection system that automatically identifies harmful and abusive content using Natural Language Processing (NLP) techniques.

The system uses TF-IDF (Term Frequency–Inverse Document Frequency) to convert textual data into numerical feature representations that capture the importance of words in a given context. These features are then classified using supervised machine learning algorithms to determine whether a given text contains cyberbullying behavior or not.

A user-friendly web-based application is developed using the Flask framework, allowing users to securely log in, input text, and receive real-time predictions along with confidence scores. The proposed model aims to improve online safety by providing an efficient, accurate, and scalable solution for cyberbullying detection.

Input  :  Social media comments

Output  :  Cyberbullying category

Tags :

#CyberbullyingDetection, #NaturalLanguageProcessing, #NLP, #TFIDF, #MachineLearning, #SupervisedLearning, #TextClassification, #HateSpeechDetection, #OnlineSafety, #ContentModeration, #FlaskApp, #PythonProject, #WebBasedML, #SocialMediaAnalytics

References:

1.Dadvar, M., Trieschnigg, D., Ordelman, R., and de Jong, F. (2013). Improving cyberbullying detection with user context. Proceedings of the 35th European Conference on Advances in Information Retrieval (ECIR), Springer.

Yin, D., Xue, Z., Hong, L., Davison, B. D., Kontostathis, A., and Edwards, L. (2009). Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB 2.0 Workshop, WWW Conference.