  Sentiment classification is an important process in understanding people's
perception towards a product, service, or topic. Many natural language
processing models have been proposed to solve the sentiment classification
problem. However, most of them have focused on binary sentiment classification.
In this paper, we use a promising deep learning model called BERT to solve the
fine-grained sentiment classification task. Experiments show that our model
outperforms other popular models for this task without sophisticated
architecture. We also demonstrate the effectiveness of transfer learning in
natural language processing in the process.
