Towards a Healthier Online Environment: Machine Learning for Detecting and Addressing Cyberbullying Comments
G S Pradeep Ghantasala, Pellakuri Vidyullatha, Rajesh Sharma R, U Ananthanagu, Akey Sungheetha, Kumar Dilip · 2024
With the internet enduring to engage in a momentous part in our lives, it is crucial to prioritize maintaining inclusive and positive interactions on internet platforms. With the rise in internet usage, platforms like Twitter have become a hub for individuals to freely express their thoughts and engage with others. However, this increased user base has also led to an increase in cyber bullying comments which could include hate speech, harassment, or assault language. Sentiment analysis comprises consuming natural language processing methods to consider the sentimentality conveyed with a slice of text, like as a comment or a tweet. It can help identify whether a comment is positive, negative, or neutral, allowing platforms to take appropriate action. This paper focusses on Machine Learning Models to deploy sentiment analysis to classify cyber bullying comments and promoting positive interactions on internet platforms. By leveraging this technology, platforms like Twitter can take proactive measures to adopt a protected and more comprehensive online environment, ultimately enhancing the user experience for everyone involved.