Fine-grained Sentiment Classification using BERT
This work addresses the problem of fine-grained sentiment classification for NLP applications, but it is incremental as it applies an existing method to a specific task.
The paper tackled fine-grained sentiment classification by applying BERT, a deep learning model, and achieved results that outperformed other popular models without complex architecture, demonstrating the effectiveness of transfer learning in NLP.
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.