Design and Optimization of Loss Functions in Fine-grained Sentiment and Emotion Analysis

Abstract

The unbalanced distribution of category labels and the correlation between these labels tend to cause over-learning issues in deep learning models. In fine-grained sentiment analysis datasets, the correlation between category labels and the heterogeneity of tag distribution are prominent. In the deep learning model, we use the adjusted circle-loss to introduce margin and gradient attenuation in the loss function to handle the challenges caused by unbalanced label distribution and non-independence between labels. This method can be well combined with pre-trained models and adapt to various learning models and algorithms. Compared with the state-of-the-art typical models, our loss function mechanism achieves significant improvement using SemEval18 and GoeEmotions by measure of Jaccard coefficient, micro-F1, and macro-F1. It implies that our solution could work efficiently for sentiment analysis and sentiment analysis tasks.