  Sentiment analysis is an important task in the field ofNature Language
Processing (NLP), in which users' feedbackdata on a specific issue are
evaluated and analyzed. Manydeep learning models have been proposed to tackle
this task, including the recently-introduced Bidirectional Encoder
Rep-resentations from Transformers (BERT) model. In this paper,we experiment
with two BERT fine-tuning methods for thesentiment analysis task on datasets of
Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for
anattached feed-forward neural network, and 2) another methodin which all BERT
output vectors are used as the input forclassification. Experimental results on
two datasets show thatmodels using BERT slightly outperform other models
usingGloVe and FastText. Also, regarding the datasets employed inthis study,
our proposed BERT fine-tuning method produces amodel with better performance
than the original BERT fine-tuning method.
