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Detection of Cyberbullying Using Machine Learning


Neel Gheewala, Yash Shah, Sakshi Patel, Prof. Brijesh Vala


Nowadays, social media has become the best leadership conference in recent years. With the widespread use of social media, cyberbullying, cyberbullying and cybercrime have increased, which has had a positive impact on people's worldview. People's health can be negatively affected by cyberbullying and can sometimes lead to mental health problems. Explicit messages about sex and rumors spread by many users are two examples of how this affects relationships. The number of researchers interested in cyberbullying has increased in recent years. One of our goals is to use natural language processing (NLP) and random forest algorithms to create a system that can identify the nature of online abuse. The rapid spread of the COVID-19 virus has changed the culture, leading to cyberbullying, especially among young people. Most teenagers follow this model. As some of the platforms that facilitate online dating grow, so does social media for bullying. The COVID-19 virus has not only caused cyberbullying but also changed the nature of human relationships online. Bullying is becoming an issue as many people start working from home. The planning process is divided into data maintenance, text search, word embedding, regression analysis and other methods. The paper mining model uses lemmatization technology, which helps improve the accuracy of the model. TF-IDF is used for word embeddings and Tf-idf gives good mathematical meaning to the model. The random forest algorithm is used for the distribution of data in the conceptual model; This will help reduce overfitting of the data in the model.


Machine learning, Analysis and Detection


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Detection of Cyberbullying Using Machine Learning. Neel Gheewala, Yash Shah, Sakshi Patel, Prof. Brijesh Vala. 2024. IJIRCT, Volume 10, Issue 2. Pages 1-7.

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