Towards a Generative Approach for Emotion Detection and Reasoning

TL;DR

This paper introduces a novel generative question-answering approach for zero-shot emotion detection and reasoning using large language models, moving beyond fixed-label classification to more flexible, context-based analysis.

Contribution

It is the first to frame emotion detection as a generative QA task, enabling joint emotion detection and reasoning with LLMs in a zero-shot setting.

Findings

Effective in zero-shot emotion detection

Outperforms fixed-label models on benchmark datasets

Provides fine-grained emotion labels and explanations

Abstract

Large language models (LLMs) have demonstrated impressive performance in mathematical and commonsense reasoning tasks using chain-of-thought (CoT) prompting techniques. But can they perform emotional reasoning by concatenating `Let's think step-by-step' to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. Existing state of the art zero-shot approaches rely on textual entailment models to choose the most appropriate emotion label for an input text. We argue that this strongly restricts the model to a fixed set of labels which may not be suitable or sufficient for many applications where emotion analysis is required. Instead, we propose framing the problem of emotion analysis as a generative question-answering (QA) task. Our approach uses a two step methodology of generating relevant context or background knowledge to answer the emotion detection question step-by-step. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts. We evaluate our approach on two popular emotion detection datasets and also release the fine-grained emotion labels and explanations for further training and fine-tuning of emotional reasoning systems.