Attention-Based Multi-Filter Convolutional Neural Network for Inappropriate Speech Detection

Nowadays, there are numerous social platforms for people to discuss and share information. However, certain users may post contents that discriminate against specific groups or spread inappropriate behaviors. Therefore, it is desired to have a detection mechanism that is able to accurately identify these offensive comments and inappropriate messages before appearing on social platforms. To address this need, we propose a novel model called Attention-based Multi-filter Convolutional Neural Network (AMCNN) for inappropriate speech detection. Our model combines CNN and BiLSTM to acquire the advantages of the two. AM CNN leverages CNN filters of various kernel sizes for convolution operations to capture the relation between words of different lengths. The convolutional features are then used as the inputs of BiLSTM, which is able to obtain the bidirectional semantics via the forward and backward directions. AMCNN further incorporates the attention mechanism to highlight the important parts of the hidden layer output for better classification. Our model can be applied to various types and lengths of text, and it is also able to support different languages. To evaluate the model performance, we conduct extensive experiments on various real-world datasets. According to the experimental results, our model achieves the best performance, which is on average 12.63% higher than the state-of-the-art approach on the F1-score indicator. We also conduct ablation analysis, and the results show that the attention mechanism and the usage of BiLSTM can considerably improve the classification performance and effectively detect inappropriate speech in sentences.