  Text classification is a popular topic of natural language processing, which
has currently attracted numerous research efforts worldwide. The significant
increase of data in social media requires the vast attention of researchers to
analyze such data. There are various studies in this field in many languages
but limited to the Vietnamese language. Therefore, this study aims to classify
Vietnamese texts on social media from three different Vietnamese benchmark
datasets. Advanced deep learning models are used and optimized in this study,
including CNN, LSTM, and their variants. We also implement the BERT, which has
never been applied to the datasets. Our experiments find a suitable model for
classification tasks on each specific dataset. To take advantage of single
models, we propose an ensemble model, combining the highest-performance models.
Our single models reach positive results on each dataset. Moreover, our
ensemble model achieves the best performance on all three datasets. We reach
86.96% of F1- score for the HSD-VLSP dataset, 65.79% of F1-score for the
UIT-VSMEC dataset, 92.79% and 89.70% for sentiments and topics on the UIT-VSFC
dataset, respectively. Therefore, our models achieve better performances as
compared to previous studies on these datasets.
