An Intense Study of Machine Learning Research Approach to Identify Toxic Comments

Abstract

A large number of online public domain comments are usually constructive, but a significant proportion is toxic. The comments include several errors that allow the machine-learning algorithm to train the data set by processing dataset with numerous variety of tasks, in the method of conversion of raw comments previously feeding it to Classification models using a ML method. In this study, we have proposed classification of toxic comments using a ML approach on a multilinguistic toxic comment dataset. The logistic regression method is applied to classify processed dataset, which will distinguish toxic comments from non-toxic comments. The multi-headed model comprises toxicity (obscene, insult, severe toxic, threat, & identity-hate) or Nontoxicity Estimation. We have implemented four models (LSTM, GRU RNN, and BiLSTM) and detected the toxic comments. In Python 3, all models have a simple structure that can adapt to the resolution of other tasks. The classification problem resolution findings are presented with the aid of the proposed models. It has been concluded that all models solve the challenge effectively, but the BiLSTM is the most effective to ensure the best practicable accuracy.