Language Representation Models for Fine-Grained Sentiment Classification

Understand

Sentiment classification is a quickly advancing field of study with applications in almost any field.

• While various models and datasets have shown high accuracy inthe task of binary classification, the task of fine-grained sentiment classification is still an area with room for significant improvement.
• Analyzing the SST-5 dataset,previous work by Munikar et al.
• (2019) showed that the embedding tool BERT allowed a simple model to achieve state-of-the-art accuracy.