  Society needs to develop a system to detect hate and offense to build a
healthy and safe environment. However, current research in this field still
faces four major shortcomings, including deficient pre-processing techniques,
indifference to data imbalance issues, modest performance models, and lacking
practical applications. This paper focused on developing an intelligent system
capable of addressing these shortcomings. Firstly, we proposed an efficient
pre-processing technique to clean comments collected from Vietnamese social
media. Secondly, a novel hate speech detection (HSD) model, which is the
combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed
for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal
with imbalanced data to improve the performance of classification models.
Besides, various experiments were conducted as baselines to compare and
investigate the proposed model's performance against state-of-the-art methods.
The experiment results show that the proposed PhoBERT-CNN model outperforms
SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark
datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming
HSD application to demonstrate the practicality of our proposed system.
