Abstract
Web-based life has turned into an integral part of the regular day-to-day existence of a large number of individuals around the globe. Online commenting spaces generate a plethora of expressive content in the public domain, which contributes to a healthy environment for humans. However, it also has threats and dangers of cyberbullying, personal attacks, and the use of abusive language. This motivates industry researchers to model an automated process to curb this phenomenon. The aim of this paper is to perform multi-label text categorization, where each comment could belong to multiple toxic labels at the same time. We tested two models: RNN and LSTM. Their performance is significantly better than that of Logistic Regression and ExtraTrees, which are baseline models.
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Shah, D.K., Sanghvi, M.A., Mehta, R.P., Shah, P.S., Singh, A. (2021). Multilabel Toxic Comment Classification Using Supervised Machine Learning Algorithms. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_3
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DOI: https://doi.org/10.1007/978-981-15-7106-0_3
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