  In the text classification problem, the imbalance of labels in datasets
affect the performance of the text-classification models. Practically, the data
about user comments on social networking sites not altogether appeared - the
administrators often only allow positive comments and hide negative comments.
Thus, when collecting the data about user comments on the social network, the
data is usually skewed about one label, which leads the dataset to become
imbalanced and deteriorate the model's ability. The data augmentation
techniques are applied to solve the imbalance problem between classes of the
dataset, increasing the prediction model's accuracy. In this paper, we
performed augmentation techniques on the VLSP2019 Hate Speech Detection on
Vietnamese social texts and the UIT - VSFC: Vietnamese Students' Feedback
Corpus for Sentiment Analysis. The result of augmentation increases by about
1.5% in the F1-macro score on both corpora.
