Abstract
Social networking has become a significant component of life for all. With increasing accessibility to the Internet, there is a huge amount of hatred and toxicity globally on online platforms. People tend to post toxic comments, hiding behind anonymous identities. Therefore, it is very much required to filter out the toxicity and avoid unnecessary hate on online social media platforms, so that the actual purpose of these platforms, which is to facilitate effective conversations among communities and expressing oneself freely, can be fulfilled. The research work aims to build and train various classification models, namely SVM, logistic regression, naive Bayes, XGBoost, and bidirectional LSTM (long short-term memory), which assist in the classification of toxic comments in different subcategories. A comparative evaluation of these classification models using two datasets is done based on their accuracy. With these classification models’ help, we can prevent online harassment and abuse and create a safer Internet environment.
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Gupta, S., Goel, M., Rathee, N. (2022). Machine Learning Approach to Classify Toxic Comments on Social Media Platforms. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_34
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DOI: https://doi.org/10.1007/978-981-16-5348-3_34
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