Toxic comment detection is an important work to prevent the negative impact of social media platform on users

and it is also one of the important fields of natural language processing. In order to solve the problems of unstable model accuracy and low accuracy of boosting ensemble model when an individual classifier detects toxic comments

a stack generalization with heterogeneous classifiers is proposed. In this method

the classification problem of multi-label toxic comments is transformed into binary categories by using deep recurrent neural network

which prevents the model accuracy from being unstable. Individual classifiers called GRU (Gated Recurrent Unit) and NB-SVM (Naïve Bayes-Support Vector Machine) are used during stacked generalization in order to embody the differences on model structure and classification deviation of individual classifiers

the goal is to improve the model accuracy. Experimental results on Wikipedia toxic comments show that the proposed method has better than boosting ensemble

which reports that stacked generalization of heterogeneous classifiers is feasible and effective for toxic comments detection.