One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025)

Abstract: Emotion cause analysis is a critical topic in natural language processing. Key tasks include emotion cause extraction (ECE), emotion-cause pair extraction (ECPE), social emotion cause identification (SECI) as well as social emotion mining and its cause identification (SEMCI). While current emotion cause analysis methods often focus on task-specific model design, they tend to overlook the underlying common ground across these tasks rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. Drawing inspiration from this theory, in this paper, we propose a unified model capable of tackling diverse emotion cause analysis tasks, which constructs the emotion cognitive structure through LLM-based in-context learning. To mitigate the hallucination inherent in LLMs, we introduce a self-promote mechanism built on iterative refinement. It dynamically assesses the reliability of substructures based on their cognitive consistency and leverages the more reliable substructures to promote the inconsistent ones. Experimental results on multiple emotion cause analysis tasks ECE, ECPE, SECI and SEMCI demonstrate the superiority of our unified model over existing SOTA methods and LLM-based baselines.

## introduction with the continuous developments of the internet and social media, the analysis of emotions and their causes in text has attracted increasing research attention and widely applied to a variety of domains. previously, multiple emotion cause analysis tasks have been proposed, including emotion cause extraction (ece) @xcite , emotion cause pair extraction (ecpe) @xcite , social emotion cause identification (seci) @xcite , and social emotion mining and its cause identification (semci) @xcite figure 1 : diverse emotion cause analysis tasks share a common underlying emotion cognitive structure and thus can be addressed in a unified manner via cognitive structure modeling. @xcite . among them, ece and ecpe extract the emotions experienced by characters in textual documents, which are usually explicitly expressed by the author. in contrast, seci and semci identify the emotions evoked to the reader (i.e., the public) from textual descriptions, which are often implicitly conveyed. existing research on emotion cause analysis has solely focused on specific computational models tailored to individual tasks @xcite @xcite , overlooking the underlying common ground rooted in emotion theories. appraisal theories are well-established and the most influential theoretical models for explaining the antecedents and consequences of human emotions @xcite @xcite . they argue that emotions arise from the cognitive pro-cess of the subjective assessment of personal relationships with the environment, including not only current conditions but also events that lead to this state and future prospects. appraisal is informed by this cognitive process and maps its characteristics onto a common set of intermediate terms (i.e., appraisal variables). among cognitive appraisal theories, the cognitive structure of emotions model @xcite , proposed by ortony, clore and collins (i.e., the occ model), is well-developed and the most widely adopted in computational emotion modeling @xcite . the occ model identifies the underlying cognitive structure of different emotion types with elicitation conditions, represented as appraisal dimensions with corresponding values. specifically, desirability, praise/blameworthiness and likelihood are the three key appraisal dimensions that can lead to certain major emotion types. the desirability dimension is associated with the goal attainments of agents, with desirable and undesirable values; the praise/blameworthiness dimension is associated with certain standards of actions, with praiseworthy and blameworthy values; and the likelihood dimension is associated with the expectation of events, with certain and uncertain values. for example, in figure 1 , past events in the text happened with certainty; the flood disaster is undesirable to the public, leading to distress; the rescue operation is a praiseworthy deed, leading to admiration toward the rescuers. in addition, the combination of these different appraisal dimensions and their values can further elicit compound emotions, such as gratitude in figure 1 , which is elicited by dimensional values desirable and praiseworthy of the character in the text. inspired by the occ model, we take the emotion cognitive structure (i.e., ecs) as the common cognitive representation and design a unified model to construct the underlying ecs via modeling the process of cognitive appraisal, emotion elicitation, and cause analysis, with the appraisal perspective (whether from the public, author, or characters in text) being incorporated as an input variable, thereby addressing diverse emotion cause analysis tasks. previous emotion cause analysis methods typically involved training or fine-tuning discriminative models @xcite @xcite , which primarily captured linguistic features but failed to account for the inherent cognitive structure of emotion. recently, large language models (i.e., llms) have demonstrated human-like cognitive reasoning capabilities @xcite @xcite . this progress has positioned llm-based in-context learning (i.e., icl) @xcite @xcite as a new paradigm in natural language processing. therefore, llms can be instructed to perform emotion cognitive reasoning @xcite . however, they still face the challenge of hallucinations @xcite , which can be classified into two types: (1) random errors, and (2) the lack of emotion cognitive capabilities in specific situations. although some methods improve llms' performance on complex reasoning tasks by employing strategies like step-by-step reasoning @xcite and problem decomposition @xcite , they remain limited by hallucinations caused by randomness. moreover, other methods improve the reliability of llms' outputs via self-refinement @xcite or consistency checks @xcite ), yet they do not fundamentally enhance the emotion cognitive capabilities of llms. to tackle these challenges, we propose a self-promote emotion cognitive structure (specs), implemented by llm-based icl. specifically, cause, text and scene-level emotion cognitive reasoning is iteratively executed to update the corresponding substructures and refine the random errors. furthermore, to better mitigate hallucinations arising from the lack of cognitive capabilities, we propose a self-promote mechanism: first, cognitive consistency across iterations is calculated to assess the reliability of each substructure in ecs. subsequently, since the cognitive capabilities of llms in the icl paradigm largely depend on the few-shot demonstrations, we select demonstrations from high-consistency substructures to promote the low-consistency ones, considering both the cognitive consistency of the candidates and the context relevance across different substructure levels. with this mechanism, llms will achieve a promotion of emotion cognitive capabilities from the structures constructed by themselves, thereby further facilitating the refinement of ecs. our main contributions are as follows: clause level: cognitive appraisal response r: '…, so my answer is [yes].' instruction i demos θ: { (q1, r1), (q2, r2) ,… } instruction i demos θ: { (q1, r1), (q2, r2) ,… } question q: 'decide whether clause [c1] describes action [action] that be appraised as [praiseworthy] by [p]?' instruction i demos θ: { (q1, r1), (q2, r2) ,… } question q: 'decide whether clause [c1] describes action [action] that be appraised as [praiseworthy] by [p]?' [admiration] is [action].' instruction i demos θ: { (q1, r1), (q2, r2) ,… } question q: '[praiseworthy] [c1, c2, ...] make [p] feel [admiration]. what's the cause [action]?' response r: 'the cause of [admiration] is [action].' scene level: cause analysis instruction i demos θ: { (q1, r1), (q2, r2) ,… } question q: '[praiseworthy] [c1, c2, ...] make [p] feel [admiration]. what's the cause [action]?' response r: 'the cause of [admiration] is [action].' clause 1 undesirable clause 2 praiseworthy clause 3 desirable distress admiration gratitude • • • • • • • • • hope emotion cognitive structure g scene-level substructures caused by elicited by text-level substructures clause-level substructures cause scenes refine cause scenes refine elicited emotions refine elicited emotions refine appraisal dimensions refine appraisal dimensions refine llm-based emotion cognitive reasoning event 1 flood disaster event 1 flood disaster action rescue operation action rescue operation event 2 residents saved ## method in this paper, we aim to design a unified model capable of addressing diverse emotion analysis tasks, including ece, ecpe, seci and semci. for ece and seci tasks, given the text d and an emotion e, the model outputs a set of cause clauses c. for the ecpe and semci tasks, given the text d only, the model outputs emotion-cause pairs {(e, c e )}. we model the emotion cognitive structure within the paradigm of llm-based icl. moreover, we propose a self-promote framework to address the hallucination challenge. ## related work cognitive appraisal theories in cognitive psychology, the causes and effects of emotions have been extensively studied through cognitive appraisal theories @xcite @xcite . these theories argue that emotions arise from the subjective assessment of personal relationships with the environment, including not only current conditions, but also events that lead to this state and future prospects @xcite . appraisal itself is influenced and guided by cognitive processes, which map the characteristics of these processes into a common set of intermediate terms (i.e., appraisal variables). among cognitive appraisal theories, the cognitive structure of emotions model (i.e., the occ model) proposed by @xcite is one of the most well-developed psychological emotion models and widely adopted in computational modeling of emotions @xcite . the occ model identifies the underlying cognitive structure of 22 emotion types: well-being (e.g. joy, distress), prospect-based (e.g. hope, fear), attribution (e.g. admiration, reproach), well-being/attribution compound (e.g. gratitude, anger), fortunes-of-others and attraction (e.g. love, hate). the occ model has been widely applied to various computational tasks, including character modeling (klinkert and clark, 2021), robot-human communication @xcite , and text mining @xcite . ## conclusion this paper presents a unified model capable of diverse emotion cause analysis tasks. our model employs the llm-based in-context learning to iteratively construct the underlying emotion cognitive structure, which are often overlooked by existing methods. to mitigate the hallucination problem in llms, we designed a self-promote mechanism, which enhances llms' emotion cognitive capability for cognitively inconsistent reasoning cases, without requiring additional external knowledge or training. experimental results show that our method outperforms strong baselines across all four tasks, including supervised discriminative models and llm-based icl frameworks. ablation studies further validate the effectiveness of each component in our model.