Towards Interpretable Emotion Classification: Evaluating LIME, SHAP, and Generative AI for Decision Explanations

This paper explores the classification of multi-label emotions utilizing fine-tuned RoBERTa base and zero-shot GPT4 models, with experiments conducted on the SemEval 2018 E-c dataset encompassing 11 emotions, where more than one label is allowed for a text. Employing SHAP and LIME for RoBERTa explanations and generative AI for GPT4, we assess the sufficiency of explanations using the BERT score metric. We show the explanations generated by LIME and SHAP visually using different plots. The BERT score indicates that generative AI produces better explanations than the statistical models, providing deeper insights into emotion selection, with a BERT score of 59.66% compared to SHAP-RoBERTa's 54.17% and LIME-RoBERTa's 53.22%. This shows the potential of generative AI in revealing the reasoning behind decisions within complex emotional contexts. Though the performance is superior, we also discuss the limitations of these models that hinder wide-scale adoption.