Comparative Analysis of Emotion Recognition Using Large Language Models and Conventional Machine Learning

Abstract

Emotions are significant aspects of human existence and influence interaction between individuals and groups, influencing how we think and behave. In this research, we aim to use conventional and neural network models to identify emotions from textual data and compare which performed best. The Go Emotions dataset contained 27 different emotions across 58,000 samples. The approach involves modelling the conventional machine learning models and the neural network-based models and comparing the results over test dataset and choosing the best model. Upon comparing the classification reports for the conventional and neural network-based models on the Ekman taxonomy, conventional machine learning algorithms were outperformed by neural network-based models which gained almost 10% more than conventional models. Conventional models averaged the values around 50% of macro-average F1-score except for the KNN classifier which performed poorly getting the macro-average F1-score of 21%. BERT classifier with Ekman taxonomy including neutral emotion had a macro-average precision of 55% and a sensitivity of 68%. This classifier also outperformed the macro-average F1-score by 106 61%. While the RoBERTa classifier had a macro-average precision of 65%, the recall, or sensitivity, was found to be 53%. This study clearly states that neural network-based models outperformed conventional models. Our study proposed BERT model which achieved a macro-average F1-score of 0.50 across Go Emotion taxonomy.