BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text

Abstract

Cyberbullying has become increasingly common due to the extent and anonymity afforded to users by online social media, and poses a significant risk to the physical and mental health of people. In this study, we propose an ISSA-based model to detect cyberbullying in Chinese text (ISSA-BiLSTM-Attention-CNN) that can determine whether a given comment reflects cyberbullying. The model contains an attention mechanism and the improved sparrow search algorithm (ISSA) for optimization that enables it to focus on important textual information and make full use of the optimal hyperparameters. Before applying the CNN to collect and learn a sufficient number of local features, the model initially uses the bidirectional LSTM (BiLSTM) to concatenate the results of forward and backward processing of the given text. The results of experiments showed that the proposed method can outperform baseline methods, with an accuracy of 90.2% and an f-measure of 89.9%.