Fine-grained Sentiment Classification using BERT

TL;DR

This paper leverages BERT for fine-grained sentiment classification, demonstrating that transfer learning enhances performance without complex architectures.

Contribution

It introduces BERT-based fine-grained sentiment classification and highlights the effectiveness of transfer learning in NLP tasks.

Findings

BERT outperforms other models in sentiment classification

Transfer learning significantly improves results

Simple architectures can achieve high accuracy

Abstract

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.