关键词: 堆叠泛化, 恶意评论, 循环神经网络, NB-SVM, 词嵌入

Abstract: 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.

Key words: stacked generalization, toxic comments, recurrent neural network, NB-SVM