This paper presents a deep learning-based pipeline for categorizing Bengali
toxic comments, in which at first a binary classification model is used to
determine whether a comment is toxic or not, and then a multi-label classifier
is employed to determine which toxicity type the comment belongs to. For this
purpose, we have prepared a manually labeled dataset consisting of 16,073
instances among which 8,488 are Toxic and any toxic comment may correspond to
one or more of the six toxic categories - vulgar, hate, religious, threat,
troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT
Embedding achieved 89.42% accuracy for the binary classification task while as
a multi-label classifier, a combination of Convolutional Neural Network and
Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism
achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the
predictions and interpret the word feature importance during classification by
the proposed models, we utilized Local Interpretable Model-Agnostic
Explanations (LIME) framework. We have made our dataset public and can be
accessed at -
https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classificatio