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.

本研究利用先进的预处理技术和多种实验验证，提出一种基于 PhoBERT 和 Text-CNN 模型的 HSD 模型来检测越南社交媒体上的仇恨言论，在应对数据不平衡的同时提高了分类模型的性能，通过实验结果验证，该模型在 ViHSD 和 HSD-VLSP 等数据集上获得了更高的 F1 得分，并且进一步构建了一种实用的流媒体应用程序以演示其实用性。