Towards a Generative Approach for Emotion Detection and Reasoning

• URL: http://arxiv.org/abs/2408.04906v1
• Date: Fri, 9 Aug 2024 07:20:15 GMT
• Title: Towards a Generative Approach for Emotion Detection and Reasoning
• Authors: Ankita Bhaumik, Tomek Strzalkowski,
• Abstract summary: We introduce a novel approach to zero-shot emotion detection and emotional reasoning using large language models. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts.
• Score: 0.7366405857677227
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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