Venue: NAACL 2025

# One Unified Model for Diverse Tasks: Emotion Cause Analysis

via Self-Promote Cognitive Structure Modeling

Zhaoxin Yu1,2, Xinglin Xiao3, Wenji Mao1,2

Corresponding author \({}^{1}\)State Key Laboratory of Multimodal Artificial Intelligence Systems,

Institute of Automation, Chinese Academy of Sciences

\({}^{2}\)School of Artificial Intelligence, University of Chinese Academy of Sciences

\({}^{3}\)Beijing Wenge Technology Co., Ltd

{yuzhaoxin2024,wenji.mao}@ia.ac.cn;xinglin.xiao@wenge.com

###### 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.

## 1 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) Lee et al. (2010); Gui et al. (2018), emotion cause pair extraction (ECPE) Xia and Ding (2019), social emotion cause identification (SECI) Xiao et al. (2023a), and social emotion mining and its Cause Identification (SEMCI) Xiao et al. (2023b). 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 Gui et al. (2017); Ding et al. (2020); Xiao et al. (2023a, b), 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 Lazarus (1991); Scherer et al. (2001); Sloman et al. (2005); Gratch and Marsella (2013). They argue that emotions arise from the cognitive pro

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.



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 [14], proposed by **O**trony, **C**lore and **C**ollins (i.e., **the OCC model**), is well-developed and the most widely adopted in computational emotion modeling [23]. 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 _Praiseeworthy_ 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 [50, 69, 78], 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 [26, 77, 76]. This progress has positioned LLM-based in-context learning (i.e., ICL) [15, 16, 17] as a new paradigm in natural language processing. Therefore, LLMs can be instructed to perform emotion cognitive reasoning [76, 78]. However, they still face the challenge of hallucinations [77], 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 [68, 79] and problem decomposition [14, 76], they remain limited by hallucinations caused by randomness. Moreover, other methods improve the reliability of LLMs' outputs via self-refinement [11] or consistency checks [76], yet they do not fundamentally enhance the emotion cognitive capabilities of LLMs.

To tackle these challenges, we propose a **S**elf-**P**romote **E**motion **Co**gnitive **S**ructure **(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:

* Based on the cognitive appraisal theory, we identify the common ground underlying diverse emotion cause analysis tasks ECE, 

ECPE, SECI, and SEMCI, and propose a unified model addressing these tasks via cognitive structure modeling.
* We leverage the in-context learning capabilities of LLMs for clause, text, and scene levels emotion cognitive reasoning, and mitigate the hallucination challenge of LLMs through a novel self-promote mechanism.
* We conduct experiments across diverse emotion cause analysis tasks, and the results verify that our unified model significantly outperforms the existing SOTA methods for each individual task as well as LLM-based baselines.

## 2 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 \({\cal C}\). For the ECPE and SEMCI tasks, given the text \(D\) only, the model outputs emotion-cause pairs \(\{(E,{\cal C}_{E})\}\).

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.

### LLM-based Emotion Cognitive Reasoning

The OCC model uses appraisal dimension variables to represent the cognitive appraisal process and establishes a mapping between these variables (or their combinations) and emotions of 22 types. As shown in Table 1, we focus on three key appraisal dimension variables: _Desirability_, _Praise/Blameworthiness_, and _Likelihood_, corresponding to 12 common emotion types. Figure 1 illustrates an example: _Praise_ actions that _Certainly_ led to _Desirable_ events may evoke the emotion of Gratitude.

Guided by the OCC model, we designed three fundamental emotion cognitive reasoning operations to construct the emotion cognitive structure (i.e., _ECS_), as shown in Figure 2.

Clause Level: Cognitive AppraisalFor each clause, the model respectively appraises the values of three appraisals aforementioned, thereby constructing clause-level substructures within the _ECS_.

Text Level: Emotion ElicitationApplying OCC

Figure 2: Our proposed emotion cognitive structure consists of a three-level substructure: clause, text, and scene, which respectively model the processes of cognitive appraisal, emotion elicitation, and cause analysis in human emotional cognition.

 

rules, we compute potential emotions based on the appraisals of clauses and their combinations. The model subsequently evaluates the causal relationships between these appraisals and the corresponding emotions, verifying their presence and constructing text-level substructures within the _ECS_.

Scene Level: Cause AnalysisBased on the substructures from the previous two levels (clauses that trigger specific appraisals and the corresponding emotions), the model extracts or generalizes the causes of emotions at the scene level.

The emotion cognitive reasoning of the above process is implemented within the paradigm of LLM-based ICL:

\[r={\cal M}(I||\Theta||q) \tag{1}\]

\(I\), \(\Theta\) and \(q\) represent the system instruction, few-shot demonstrations set, and the input question, respectively. Due to the inherent randomness, LLMs may occasionally produce erroneous reasoning responses. Consequently, _ECS_ constructed through a single pipeline may lack full reliability. To address this issue, we propose executing the aforementioned operations iteratively. The previously constructed _ECS_ serves as prior knowledge for the current step, and its output modifies the corresponding substructures, enabling the refinement of _ECS_.

### Self-Promote ECS Refinement

Iterative refinement can effectively mitigate hallucinations arising from inherent randomness. However, hallucinations still persist in reasoning cases that exceed the cognitive capabilities of LLMs. This phenomenon parallels human reasoning: under uncertainty or low confidence, humans are unable to provide a consistent and correct answer to repeated questions [25].

To further enhance the cognitive capabilities of LLMs in the aforementioned situations, we propose a self-promote _ECS_ refinement framework, which consists of two stages: self-assessment and self-promote. Figure 3 illustrates the key concept of this framework.

Self-Assessment During Iterative RefinementIn this stage, after iteratively performing \(T_{0}\) iterations of _ECS_ refinement with manually designed standard demonstrations, _ECS_ of multiple samples are initially constructed. For each substructure \(g\) within each _ECS_, we calculate the cognitive consistency \(\phi_{g}\) across iterative refinements:

\[f_{\phi}(\{r^{\tau}_{g}\}_{\tau=1}^{T})=\sum_{\tau=1}^{T-1}(\frac{1}{1+e^{- \tau}}\cdot\frac{r^{\tau}\cdot r^{T}}{\|r^{\tau}\|\|r^{T}\|}) \tag{2}\]

Here, \(\{r^{\tau}_{g}\}_{\tau=1}^{T}\) denotes the multi-round outputs of emotion cognitive reasoning corresponding to substructure \(g\), which are encoded into fixed-length vectors by Sentence-BERT [46]. We also designed iteration weights \(\frac{1}{1+e^{-\tau}}\), which increase as the structure undergoes iterative refinement, assigning greater importance to later iterations. As many studies have shown that the accuracy of LLM-generated content is positively correlated with consistency across iterations [78, 9, 10, 11] , we use cognitive consistency to represent the reliability of each substructure: if \(\phi_{g}>\alpha\) (where \(\alpha\) is the consistency threshold), substructure \(g\) will be classified as consistent; otherwise, it will be classified as inconsistent.

Self-Promote In-Context LearningIn emotion cognitive reasoning, the selection of few-shot demonstrations for ICL significantly impacts the cognitive capabilities of LLMs. Therefore, we pro

Figure 3: The overall architecture of our self-promote mechanism for cognitively inconsistent substructures.

 

pose extracting demonstrations from cognitively consistent substructures to promote the inconsistent ones. Cognitive consistency and context relevancy at different levels will be simultaneously considered as factors in the selection of demonstrations. Specifically, we compute the score by performing a weighted summation of the cognitive consistency \(\phi_{j}\) of the consistent candidate substructure \(g_{j}\) and the context relevancy \(\rho_{(i,j)}\) with the target substructure \(g_{i}\):

\[\textit{SCORE}=\delta_{0}\cdot\phi_{j}+\sum_{l=1}^{3}(\delta_{l}\cdot\rho_{( i,j)}^{l}) \tag{3}\]

Here, \(\delta_{0},\cdots,\delta_{3}\) represent the weights of cognitive consistency and the context relevancy at the three levels of _ECS_: clause, text and scene, respectively. The cosine similarity is used to measure the relevance of the textual context:

\[\rho_{(i,j)}^{l}=\frac{t_{i}^{l}\cdot t_{j}^{l}}{\|t_{i}^{l}\|\|t_{j}^{l}\|} \tag{4}\]

Here, \(t_{i}^{l}\) and \(t_{j}^{l}\) represent the embedding of textual context at level \(l\).

In the subsequent _ECS_ iterative refinement process, for each inconsistent substructure, a set of top-ranked positive and negative demonstrations is selected from all consistent substructures at the same level from other _ECS_. This mechanism is executed dynamically to continuously update each _ECS_'s consistency status, allowing the LLM to learn from its own high-consistency reasoning and achieve self-promote in emotion cognition.

### Unified ECS Modeling

Formally, Algorithm 1 outlines the complete process of our proposed model. The model conducts explicit emotion cognitive reasoning within the LLM-based ICL paradigm, denoted as \(\mathcal{M}_{G_{k}}(I||\Theta||q_{g})\). Here \(\Theta=\{\theta_{1},\theta_{2},\cdots\}\) represents the set of few-shot demonstrations, and \(\mathcal{M}_{G_{k}}\) denotes the current _ECS_\(G_{k}\) serving as prior knowledge for emotion cognitive reasoning before the query \(q_{g}\).

For ECE/SECI tasks, the clause-level substructure corresponding to the given emotion \(E\) is extracted as cause clauses. For ECPE/SEMCI tasks, in addition to the clauses, emotion expressions at scene or text level is also extracted.

```
Input: Dataset \(\mathcal{D}=\{D_{k}\}_{k=1}^{N_{D}}\) Output: ECE/SECI task: cause clauses \(\mathcal{C}_{k}\) for each \(D_{k}\); ECPE/SEMCI task: emotion-clause pairs \(\{(E,\mathcal{C}_{E})\}_{k}\) for each \(D_{k}\)// Initialization of ECS
1foreachiteration\(\tau\)in\(\{1,\cdots,T_{0}\}\)do
2foreach\(k\)in\(\{1,\cdots,N_{D}\}\)do
3Reasoning: perform emotion cognitive reasoning \(r_{y}^{r}\leftarrow\mathcal{M}_{G_{k}}(I||\Theta||q_{g})\) at the corresponding level for each substructure \(g\) in \(G_{k}\);
4Refinement:update \(G_{k}\) by \(r_{g}^{r}\);
5
6
7
8
9
10 endfor
11
12 endfor
13
14 endfor
15
16 endfor
17
18 endfor
19
20 endfor
21return\(\mathcal{A}\)
```

**Algorithm 1**Process of Unified Model

For ECE/SECI tasks, the clause-level substructure corresponding to the given emotion \(E\) is extracted as cause clauses. For ECPE/SEMCI tasks, in addition to the clauses, emotion expressions at scene or text level is also extracted.

## 3 Experiments

### Dataset and Metrics

We evaluated the performance of our proposed method on the ECE, ECPE, SECI, and SEMCI tasks. Experiments were conducted on two publicly available emotion cause analysis datasets. For ECE and ECPE tasks, we conduct experiments based on the **ECPE Chinese dataset1** Xia and Ding 

2019). The dataset is built upon the classic benchmark ECE corpus for the ECE task and includes a series of Chinese city news from NEWS SINA, along with annotations for emotion categories and emotional cause clauses. For SECI and SEMCI tasks, we conduct experiments based on the **SECI dataset2**Xiao et al. (2023). The dataset contains a series of Chinese online news documents, which evoke six social emotion types, along with annotations for the cause clauses corresponding to specific emotions. The details of datasets will be provided in Appendix C.

Footnote 2: [https://github.com/xxllll/social-emotion-cause-identification](https://github.com/xxllll/social-emotion-cause-identification)

The precision (\({\bf P}\)), recall (\({\bf R}\)) and F1 score (\({\bf F1}\)) defined in Gui et al. (2018); Xia and Ding (2019); Xiao et al. (2023) are used to evaluate the performance of the four tasks.

### Baseline Methods

We designed comparative experiments to validate the superiority of the proposed self-promote emotion cognitive structure (SPECS). Firstly, we adopted several high-performing supervised discriminative models in individual tasks as baselines:

**ECE**: RTHN Xia et al. (2019), FSS-GCN Hu et al. (2021), EF-BHA Hu et al. (2021) and UECA-Prompt Zheng et al. (2022).

**ECCPE**: UECA-Prompt Zheng et al. (2022), ECPE-MTL Li et al. (2023), CD-MRC Cheng et al. (2023), MV-SHIF Yang et al. (2024) and MGCL Yu et al. (2024) .

**SECI**: RTHN Xia et al. (2019), FSS-GCN Hu et al. (2021), BERT-encoded MLP Devlin et al. (2019) and CogLES Xiao et al. (2023).

**SEMCI**: BERT-encoded MLP Devlin et al. (2019) and JointPSEC Xiao et al. (2023).

Likewise, we evaluated the performance of LLM-based in-context learning methods, including Standard ICL Ouyang et al. (2022), Chain-of-Thought Kojima et al. (2022), KATE Liu et al. (2021), Self-Refine Madaan et al. (2024), Self-Consistency Wang et al. (2023) and COSP Wan et al. (2023). Qwen2-7B3, an open-source model and GPT-4o4, a closed-source model, were selected as the LLMs.

Footnote 4: [https://platform.openai.com/docs/models/grt-4o](https://platform.openai.com/docs/models/grt-4o)

### Main Results

Comparison with supervised discriminative modelsAs shown in Table 2, compared to supervised discriminative models, our SPECS model achieves superior overall performance, with F1-score improvements of 2.87%, 4.78%, 3.26% and 7.78% on the ECE, ECPE, SECI, SEMCI tasks, respectively, over SOTA methods. This improvement is primarily attributed to a significant increase in Precision, which surpasses the SOTA methods by 4.93%, 4.93%, 4.32% and 6.37% on the four tasks, respectively. This is because modeling the underlying emotion cognitive structure enables the capture of deeper cognitive relationships beyond linguistic features, which are solely the focus of supervised discriminative models.

Comparison with LLM-based ICL frameworks We also evaluated the performance of LLM-based ICLR frameworks on these four tasks. The experimental results show that standard ICL under performs supervised discriminative models. This is primarily due to LLM hallucinations, where many

\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Tasks** & **Methods** & **P** & **R** & **F1** \\ \hline \multirow{5}{*}{**ECE**} & RTHN\({}^{\dagger}\) & 76.97 & 76.62 & 76.77 \\  & FSS-GCN & 78.05 & 76.13 & 77.08 \\  & EF-BHA\({}^{\circ\dagger}\) & 79.38 & 78.08 & 78.68 \\  & UECA-Prompt\({}^{\diamond}\) & 82.67 & 84.33 & 83.49 \\ \cline{2-5}  & SPES\({}_{\text{(wf Overall-7B)}}\) & 86.17 & 83.88 & 85.01 \\  & SPES\({}_{\text{(wf PT+4o)}}\) & **87.60** & **85.16** & **86.36** \\ \hline \multirow{5}{*}{**ECPE**} & UECA-Prompt\({}^{\diamond}\) & 72.19 & 78.04 & 75.00 \\  & ECPE-MTL\({}^{\diamond}\) & 75.61 & 75.04 & 75.32 \\  & CD-MRC\({}^{\diamond}\) & 82.53 & 77.60 & 79.99 \\  & MV-SHIFT\({}^{\dagger}\) & 80.80 & 78.40 & 79.60 \\  & MGCL\({}^{\dagger}\) & 83.41 & 80.13 & 81.66 \\ \cline{2-5}  & SPES\({}_{\text{(wf Overall-7B)}}\) & 85.16 & 81.02 & 83.03 \\  & SPES\({}_{\text{(wf OPT-4o)}}\) & **86.30** & **83.71** & **84.99** \\ \hline \multirow{5}{*}{**SECI**} & RTHN\({}^{\dagger}\) & 65.42 & 63.03 & 64.20 \\  & FSS-GCN\({}^{\dagger}\) & 65.79 & 64.10 & 64.93 \\  & BERT+MLP\({}^{\triangleright}\) & 75.47 & 76.62 & 76.04 \\  & CogLES\({}^{\circ\dagger}\) & 80.41 & 80.13 & 80.23 \\ \cline{2-5}  & SPES\({}_{\text{(wf Open2-7b)}}\) & 82.40 & 81.05 & 81.72 \\  & SPES\({}_{\text{(wf QT-4o)}}\) & **84.73** & **82.29** & **83.49** \\ \hline \multirow{5}{*}{**SEMCI**} & BERT+MLP\({}^{\diamond}\) & 70.34 & 69.28 & 69.81 \\  & JointPSEC\({}^{\dagger}\) & 68.02 & 67.70 & 67.86 \\ \cline{1-1} \cline{2-5}  & SPES\({}_{\text{(wf Overall-7B)}}\) & 74.94 & 71.28 & 73.06 \\ \cline{1-1}  & SPES\({}_{\text{(wf PT+4o)}}\) & **76.71** & **74.59** & **75.64** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between our methods and the baselines based on supervised discriminative models, with the best results highlighted in bold and the best results of baselines underlined. \(\diamond\) indicates the method is bert-based and \(\dagger\) indicates the results are reported in the original paper. Our proposed SPECS method is highlighted in blue in the table.

 

clauses without cognitive relationships to the emotion are mistakenly identified as causes in complex scenarios requiring multi-step emotion reasoning, providing seemingly 'plausible' explanations. Prompting strategies significantly improve LLM performance. However, these methods still require manually designed prompts for each emotion type, limiting the use of LLMs' general reasoning capabilities to create a unified model. Moreover, hallucinations remain a major issue for reasoning cases beyond LLMs' cognitive capabilities. Our proposed SPECS method effectively addresses the aforementioned issues. As shown in Table 3, SPECS based on Qwen2-7B outperforms all the best-performing baselines, achieving F1-score improvements of 2.13%, 2.42%, 2.81%, and 3.42% across the four tasks, respectively. Likewise, SPECS based on GPT-4o achieves F1-score improvements of 3.22%, 2.85%, 2.04%, and 2.29% over the best baselines across the four tasks, respectively.

### Ablation Study

Ablation Study of _Ecs_ RefinementTo further validate the contribution of our proposed _ECS_ refinement, we conducted an ablation study. The refinement of the three levels of substructures--cognitive appraisal (clause-level), emotion elicitation (text-level), and cause analysis (scene-level) was individually removed and evaluated across the four tasks. Table 4 illustrates the experimental results with Qwen2-7B. Removing the refinement of any level resulted in a performance decline. In the ECE and SECI tasks, cause analysis had the greatest impact, while emotion elicitation was more critical in the ECPE and SEMCI tasks, where emotion types are unknown. Additionally, cognitive appraisal had a stronger influence on SECI and SEMCI tasks, as social emotions and their causes are often implicit, requiring inference through cognitive appraisal.

Ablation Study of Self-Promote MechanismWe validated the effectiveness of the proposed self-promote mechanism. Figure 4 illustrates the model's performance and the consistent rate \(\frac{N_{consistent}}{N_{substructures}},\) where \(N_{consistent}\) denotes the number of cognitively consistent substructures and \(N_{substructures}\) denotes the total number of substructures. The vertical dashed line in the figure indicates the introduction of the self-promote mechanism at the 5-th iteration, which significantly improves both the model's overall performance and the consistency rate of the emotion cognitive structure. Furthermore, the experimental results reveal that model performance and consistency rate exhibit a correlated pattern, supporting our hypothesis that the higher the cognitive consistency of the substructures, the greater their reliability.

### Analysis of Consistency Threshold

To explore the the impact of the consistency threshold \(\alpha\) in our model, various \(\alpha\) values were configured to evaluate the model's performance across the four tasks. As shown in Figure 5, in the ECE and ECPE tasks, the model achieves optimal perfor

\begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Task: ECE**} & \multicolumn{3}{c}{**Task: ECPE**} & \multicolumn{3}{c}{**Task: SECH**} & \multicolumn{3}{c}{**Task: SEMCI**} \\ \cline{3-16}
**LLM** & **ICL Methods** & & \multicolumn{2}{c}{**P**} & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{9}{*}{Qwen2-7B} & Standard ICL & 72.58 & 71.24 & 71.90 & 65.03 & 69.44 & 67.16 & 65.52 & 77.58 & 71.04 & 62.83 & 64.50 & 63.65 \\  & COT & 77.73 & 76.59 & 77.16 & 74.32 & 71.28 & 72.77 & 69.78 & 75.58 & 72.56 & 65.65 & 63.09 & 64.34 \\  & KATE & 81.02 & 77.62 & 79.28 & 79.04 & 70.80 & 74.69 & 79.59 & 76.13 & 77.82 & 72.49 & 67.53 & 70.98 \\  & Self-Refine & 77.81 & 80.97 & 79.36 & 73.39 & 76.48 & 74.90 & 77.21 & 78.44 & 77.82 & 69.43 & 68.00 & 68.71 \\  & Self-Consistency & 80.27 & 82.04 & 81.15 & 79.79 & 78.91 & 79.35 & 80.07 & 77.41 & 78.72 & 68.65 & 67.79 & 68.22 \\  & COSP & 82.69 & 83.07 & 82.88 & 80.30 & 80.93 & 80.61 & 78.64 & 79.19 & 78.91 & 70.47 & 68.83 & 69.64 \\  & SPECS & **86.17** & **83.88** & **85.01** & **85.16** & **81.02** & **83.03** & **82.40** & **81.05** & **81.72** & **74.94** & **71.28** & **73.06** \\ \hline \multirow{9}{*}{GPT-4o} & Standard ICL & 77.49 & 77.93 & 77.71 & 74.05 & 77.91 & 75.93 & 72.10 & 75.33 & 73.68 & 67.29 & 68.54 & 68.56 \\  & COT & 76.58 & 78.22 & 77.39 & 76.39 & 73.47 & 74.90 & 76.00 & 76.24 & 76.12 & 70.28 & 71.51 & 70.89 \\ \cline{1-1}  & KATE & 83.80 & 80.07 & 81.89 & 80.71 & 78.94 & 79.82 & 80.57 & 77.39 & 78.95 & 71.00 & 69.94 & 70.47 \\ \cline{1-1}  & Self-Refine & 80.89 & 84.73 & 82.77 & 77.58 & **84.12** & 80.72 & 79.02 & 80.48 & 79.74 & 73.44 & 70.81 & 72.10 \\ \cline{1-1}  & Self-Consistency & 82.96 & 82.60 & 82.78 & 82.17 & 81.43 & 81.80 & 82.83 & 78.77 & 80.75 & 69.76 & 70.33 & 70.04 \\ \cline{1-1}  & COSP & 84.21 & 82.10 & 83.14 & 82.51 & 81.77 & 82.14 & 82.69 & 80.25 & 81.45 & 74.32 & 72.40 & 73.35 \\ \cline{1-1}  & SPECS & **87.60** & **85.16** & **86.36** & **86.30** & 83.71 & **84.99** & **84.73** & **82.29** & **83.49** & **76.71** & **74.59** & **75.64** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between our methods and the LLM-based ICL frameworks, with the best results highlighted in bold and the second-best results underlined. Our proposed SPECS method is highlighted in blue in the table.

 

mance at \(\alpha=0.9\), while in the SECI and SEMCI tasks, optimal performance is attained at \(\alpha=0.85\). Overall, both the precision and recall of the model exhibit an initial increase followed by a decrease as \(\alpha\) increases. On one hand, when \(\alpha\) is too large, certain substructures that have already been correctly refined through iterations may be incorrectly classified as cognitively inconsistent, leading to misguidance from other erroneous substructures in the self-promote process. On the other hand, when \(\alpha\) is too small, cognitively inconsistent structures cannot be effectively filtered out, resulting in insufficient refinement of the _ECS_.

## 4 Related Work

Cognitive appraisal theoriesIn cognitive psychology, the causes and effects of emotions have been extensively studied through cognitive appraisal theories Ortony et al. (1990); Scherer et al. (2001); Sloman et al. (2005); Gratch and Marsella (2013). These theories argue that emotions arise from the subjective assessment of personal relationships with the environment, including not only

Figure 4: The (a) F1-score and (b) the cognitively consistent rate of substructures across the four tasks through iterations. The vertical dashed line at the fifth iteration indicates the introduction of the self-promote mechanism.

\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Task: ECE**} & \multicolumn{6}{c}{**Task: ECPE**} & \multicolumn{6}{c}{**Task: SECI**} & \multicolumn{6}{c}{**Task: SEMCI**} \\ \cline{2-17}  & P & R & F1 & P & R & F1 & P & R & F1 & P & R & F1 \\ \hline SPECS & **86.17** & **83.88** & **85.01** & **85.16** & **81.02** & **83.03** & **82.40** & **81.05** & **81.72** & **74.94** & **71.28** & **73.06** \\ w/o Cognitive Analysis & 85.08 & 83.61 & 84.34 & 84.25 & 80.58 & 82.37 & 79.60 & 80.27 & 79.93 & 71.65 & 69.42 & 70.52 \\ w/o Emotion Elicitation & - & - & - & 74.26 & 80.04 & 77.04 & - & - & - & 68.43 & 68.29 & 68.36 \\ w/o Cause Analysis & 79.38 & 79.52 & 79.45 & 78.20 & 78.67 & 78.43 & 77.84 & 77.52 & 77.68 & 71.49 & 70.08 & 70.78 \\ w/o All & 77.03 & 78.14 & 77.58 & 72.56 & 76.66 & 74.55 & 75.54 & 77.20 & 76.36 & 65.43 & 67.96 & 66.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation study, with the refinement of substructures at three levels: Cognitive Appraisal (clause-level), Emotion Elicitation (text-level), and Cause Analysis (scene-level) being removed from the SPECS model respectively.

Figure 5: Our model’s performance with different consistency threshold \(\alpha\).

 

current conditions, but also events that lead to this state and future prospects (Gratch et al., 2006). 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 (Ortony et al., 1990) is one of the most well-developed psychological emotion models and widely adopted in computational modeling of emotions (Smith and Carette, 2022). 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 (Olgun et al., 2018), and text mining (Xiao et al., 2023a).

Supervised discriminative models for emotion cause analysisEmotion cause analysis has garnered significant research attention in recent years. Supervised discriminative models focus on the linguistic associations between emotion expression and their causes. Notable research approaches include feature extraction encoders (Xia et al., 2019; Hu et al., 2021), incorporating graph neural networks (Wei et al., 2020; Hu et al., 2021; Xiao et al., 2023), multi-task learning (Li et al., 2021, 2023; Xiao et al., 2023b) and query-aware method (Cheng et al., 2023; Diao et al., 2020). Additionally, in recent years, prompt tuning have demonstrated remarkable performance, such as (Zheng et al., 2022; Zhou et al., 2022; Gu et al., 2024). A common limitation of the aforementioned methods is their focus solely on linguistic-level features, lacking the ability of deeper emotion cognition and interpretability.

LLM-based in-context learning frameworksIn recent years, numerous studies have also explored various mechanisms to enhance the overall performance of LLMs, including Chain-of-thought (Wei et al., 2022), problem decomposition (Zhou et al., 2023; Wang et al., 2023), demonstrations selection (Rubin et al., 2022; Liu et al., 2021), selfconsistency (Wang et al., 2023) and self-refine (Madean et al., 2024). Similar to this work, some existing methods (Rubin et al., 2022; Liu et al., 2021; Wan et al., 2023) enhance the LLM's ICL capabilities by demonstrations selection. However, when directly applied to emotion cause analysis tasks, the aforementioned methods struggle with hallucination issues, as they do not fundamentally derive the emotion cognitive reasoning.

## 5 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.

### Limitations

Our approach's limitation lies in the simplification of the OCC model. Specifically, we focused on three appraisals--Desire, Praise/Blameworthiness, and Likelihood--to describe the cognitive appraisal process, enabling the analysis of cause for 12 emotion types. However, the comprehensive OCC model identifies the cognitive structure underlying 22 emotion types, which allows for a more detailed representation of human emotional expression. Expanding our model to incorporate the complete set of emotion types would enable more nuanced and sophisticated emotion cause analysis.

#### Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grants #72293575, #72434005, #72225011 and #62206287. We thank the anonymous reviewers for the valuable comments.

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## Appendix A Ethics Considerations

Since this work involves the fields of emotion and social computing, it is necessary to discuss the potential ethical issues. Below, we discuss these issues in four aspects: task design, data usage, implications for privacy, and implications for social, following the Ethics Sheet for Sentiment Analysis Mohammad (2022).

Task DesignThe emotion cause analysis tasks involved in our work are essentially natural language processing tasks. Our goal is solely to infer potential information embedded in the text, and it should not be interpreted as an attempt to predict an individual's emotional state.

Data UsageWe did not construct or propose any new datasets. All the data used in this work come from publicly available datasets Xia and Ding (2019); Xiao et al. (2023a), whose sources are also publicly accessible. Furthermore, all personal information related to the samples has been anonymized.

Implications for PrivacyConsider that people might not want their emotions to be inferred. The used data in our work is fixed and we do not engage in continuously gathering emotion-related information. We firmly oppose using the models discussed in this work for any applications that may infringe on personal privacy.

Implications for SocialWe have neither constructed nor applied any large-scale emotion detection systems, and we firmly oppose using the models discussed in this work for any applications that could lead to negative societal impacts.

## Appendix B Supplementary Experiment

### Validation of the Correlation Between Cognitive Consistency and the Cognitive Structure Correctness

Our proposed self-promotion mechanism assumes that the reliability of the emotion cognitive substructure correlates positively with its cognitive consistency across iterations. Experimental results in Figure 4 support this hypothesis. To further validate it, we conducted supplementary experiments. Without the self-promotion mechanism, we performed 5 iterations of ECS refinement, calculating the cognitive consistency of each sample's substructures. We then averaged these results to represent the sample's overall cognitive consistency and statistically analyzed the consistency scores 

for samples with correct versus incorrect emotion-cause predictions. As shown in Figure 6, the samples with correct predictions tend to exhibit higher cognitive consistency in four tasks, which further validates the aforementioned hypothesis.

### Evaluation with Various LLMs

To validate the robustness of our proposed method across multiple LLMs, we conducted experiments using various LLMs of different sizes beyond Qwen2-7B and GPT-4o. We selected five open-source LLMs of varying sizes5: Qwen2-1.5B6, phi3.5-3.8B7, LLaMA-3-8B8, Yi-1.5-34B9, and Qwen2-72B10, along with GPT-3.5 Turbo11, for experiments. All experiments with open-source LLMs are run on the machine containing 4 pieces of Tesla V100 (32GB) GPUs.

Footnote 5: For LLMs with size above 30B, we use 8 bit quantization.

Footnote 6: [https://huggingface.co/Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)

Footnote 7: [https://huggingface.co/microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)

Footnote 8: [https://huggingface.com/meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.com/meta-llama/Meta-Llama-3-8B-Instruct)

Footnote 9: [https://huggingface.co/01-ai/Yi-1.5-34B-Chat](https://huggingface.co/01-ai/Yi-1.5-34B-Chat)

Footnote 10: [https://huggingface.co/Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)

As shown in Figure 7, our model demonstrates significant improvements over Standard ICL with various LLMs. Additionally, we observe that the performance of Standard ICL improves with the increasing size of LLMs, enhancing their reasoning capabilities. In contrast, our model achieves the superior performance of larger LLMs even when applied to smaller-sized ones.

### Overhead Analysis

As our model is training-free, its computational overhead arises during the inference phase. We record the spatial (GPU usage of open-source LLMs) and temporal (Average iteration time per sample) overheads based on various LLMs: For Qwen2, the spatial overhead is 2.93 GB, 24.58 GB, and 76.50 GB for model sizes of 1.5B, 7B, and 72B, respectively, while the temporal overhead is 5.04 s, 8.07 s, and 77.67 s, respectively.

## Appendix C Details of Datasets

The ECPE dataset classifies emotions into five categories: happiness, sadness, disgust, fear, and surprise. However, this classification lacks the necessary granularity to capture nuanced emotional states. For example, the emotion labeled as 'hopiness' in the sentence 'somebody has earned everyone's respect' would be more precisely categorized as 'admiration'. To address this, we selected samples labeled as happiness, sadness, disgust, and fear from the dataset and manually mapped these categories to 12 emotions defined by the OCC model, such as joy, admiration, and gratitude12. To ensure fairness, we preserves the original annotations of both emotion and cause clauses. Specifically, Table 5 presents the mapping between emotion types in original dataset and emotion types in the OCC model, while Table 6 summarizes the sample distribution of these emotions in the ECEE and SECI dataset.

Footnote 12: Samples labeled as surprise were excluded due to ongoing debates surrounding the question, ’Is surprise an emotion?’ Numerous studies argue that surprise is merely an intermediate cognitive state involved in environmental appraisal, rather than a fully-fledged emotion [17, 18, 1].

Footnote 11: [https://platform.openai.com/docs/models/gtrt-3-5-turbo](https://platform.openai.com/docs/models/gtrt-3-5-turbo)

## Appendix D Details of the Model Implementation

### Data Preprocessing

Appraisal Perspective ExtractionIn ECE and ECPE tasks, the appraisal perspective is not predefined, making its extraction essential prior to reasoning. We employed the LLM-based Standard ICL method to extract the appraisal perspective, with the specific template prompts shown in Table 8. To maximize the accuracy of the reasoning results, this extraction process is repeated five times,

Figure 6: Density distribution of the average cognitive consistency within the emotion cognitive substructures for correctly and incorrectly predicted emotion causes in four tasks



and the response with the highest cumulative semantic similarity to all other responses is selected as the final answer.

Emotion Clauses InitializationIdentifying emotion clauses is crucial in the ECPE task. Before applying explicit reasoning with LLM, we trained a simple discriminative model for emotion clause initialization. The model uses a BERT encoder to generate vector representations, followed by an MLP layer to identify emotion clauses. During hyperparameter tuning, we prioritized recall to ensure accurate identification of all emotion clauses. Misclassified non-emotion clauses are filtered during the Emotion Elicitation phase, improving precision. The ECPE dataset is split using 10-fold cross-validation, with one part as the test set and the remaining nine as the training set. The model achieved 82.37% precision and 97.74% recall in the initialization phase, with false positives later filtered by the _ECS_ refinement process.

### Hyper-parameters

Table 7 presents the hyper-parameter settings for the SPECCS framework. The model's hyperparameters include \(\alpha\): the threshold of cognitive consistency; \(\delta_{0}\): the weight of cognitive consistency in demonstrations selection; \(\delta_{1},\delta_{2},\delta_{3}\): the weights of clause-level, text-level and scene-level context consistency in demonstrations selection; \(T_{0}\): the number of iterations in _ECS_ initialization phrase; \(T_{1}\): the total number of iterations; \(N_{Positive\ Denos},N_{Negative\ Denos}\): the numbers of the positive and negative demonstrations; temperature: the temperature of LLMs.

### Template prompts

Table 8 presents the template prompts for each subprocess in the SPECCS framework during LLM-based reasoning, including the system instruction \(I\) for setting background information and task configuration, as well as the user question \(q\) for presenting the analysis task. The design of prompts is based on the rules from the OCC model Ortony et al. (1990), including the definitions of appraisal dimension variables and their mappings to different emotion types. Building on this, for each reasoning subprocess in our model, we assessed from a group of candidate prompt templates and chose the best one as the final version.

Figure 7: The performance of various LLMs across four emotion cause analysis tasks, with the performance of our SPECS model and Standard ICL being represented respectively.

 



\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Emotion Types in Original Dataset** & \multicolumn{3}{c}{**Emotion-triggering Words**} \\  & **ECPE** & **SECI** \\ \hline Joy & 497 & 250 \\ Distress & 565 & 250 \\ Pride & 9 & 0 \\ Shame & 64 & 0 \\ Admiration & 23 & 250 \\ Reproach & 17 & 250 \\ Gratification & 2 & 0 \\ Remorse & 78 & 0 \\ Gratitude & 19 & 250 \\ Ange & 318 & 250 \\ Hope & 0 & 0 \\ fear & 397 & 0 \\ \hline \hline \end{tabular} 
\begin{tabular}{l l l l} \hline \hline
**Hyper-parameters** & \multicolumn{3}{c}{**Tasks**} \\ \cline{2-4}  & ECE & ECPE & SECI & SEMCI \\ \hline \(\alpha\) & 0.9 & 0.9 & 0.85 & 0.85 \\ \(\delta_{0}\) & 1 & 1 & 1 & 1 \\ \(\delta_{1}\) & 1 & 1 & 1.2 & 1.2 \\ \(\delta_{2}\) & 0.6 & 0.6 & 0.6 & 0.6 \\ \(\delta_{3}\) & 2 & 2 & 1.2 & 1.2 \\ \(T_{0}\) & 5 & 5 & 5 & 5 \\ \(T_{1}\) & 10 & 10 & 10 & 10 \\ \(N_{Pustrative Domen}\) & 2 & 2 & 1 & 1 \\ \(N_{Negative Domen}\) & 1 & 1 & 2 & 2 \\ temperature & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A statistical summary of emotion samples in the ECPE and SECI datasets.

\begin{table}
\begin{tabular}{p{113.8pt} p{113.8pt} p{113.8pt} p{113.8pt}} \hline \hline
**Emotion Types in Original Dataset** & \multicolumn{3}{c}{**Emotion-triggering Words**} & **Emotion Types in the OCC model** \\ \hline \multirow{4}{*}{**Happiness**} & _Gao1 Xings4_ (Happy), _Xi3 Tuet_ (Joy), _Xing4 Fu2_ (Happiness), ... & **Joy** \\ \cline{2-4}  & _Zun1 Jing4_ (Respect), _Qin1 Peti4_ (Admiration), _Zand Mei3_ (Braise), ... & **Admiration** \\ \cline{2-4}  & _Jiao1 Ao4_ (Pride), _Zid Haoz_ (Proud), ... & **Pride** \\ \cline{2-4}  & _Gan3 Xie4_ (Thank), _Gan3 J11_ (Appreciate), _Gan3 En1_ (Grateful), ... & **Grattitude** \\ \cline{2-4}  & _Man3 Zu2_ (Partification) & **Gratification** \\ \hline \multirow{4}{*}{**Sadness**} & _Shang1 Xin1_ (Sad), _Tong4 Ku3_ (Suffering), _Yu4 Men4_ (Depressed), ... & **Distress** \\ \cline{2-4}  & _How4 Hui3_ (Regret), _Chan4 Hun3_ (Repentance), _Ao4 Hui3_ (Remorse), ... & **Remorse** \\ \cline{2-4}  & _Neit Jiu4_ (Guilty), _Canzai Ku4_ (Shame), _Zia Ze2_ (Self-blame), ... & **Shame** \\ \hline \multirow{4}{*}{**Disgust**} & _Fan2 Men4_ (Anxiety), _Fun Chou2_ (Worry), _But Gan1 Xin1_ (Discontent), ... & **Distress** \\ \cline{2-4}  & _Bli3 Yi2_ (Contempt), _Ze2 Beit4_ (Blame), _Bu4 Man3_ (Dissatisfaction), ... & **Reproach** \\ \cline{2-4}  & _Dual Lian3_ (Lose face), _Xiul Kai4_ (Shame), ... & **Shame** \\ \cline{2-4}  & _Fen4 Hen4_ (Resentment), _Huai2 Hen4_ (Bear a grudge), _Fan2 Zaot_ (Irritation), ... & **Anger** \\ \cline{2-4}  & _Hui3 Hen4_ (Remorse) & **Remorse** \\ \hline \multirow{4}{*}{**Fear**} & _Hail Pat4_ (Feam), _Kong3 Ju4_ (Fright), _Jiao1 Lv4_ (Anxiety), ... & **Fear** \\ \cline{2-4}  & _Du4 Ki1_ (Sobbing), _Gan3 June2 Tian1 Yao4 Ta1_ (Feel like the world is falling apart) & **Distress** \\ \cline{2-4}  & _Jiong3 Po4_ (Askwardness), _Xiul Kai4 Nan2 Dangl1_ (Overwhelming shame) & **Shame** \\ \hline
**Auger** & _Fen4 Na4_ (Anger), Qi4 Nao3 (Irritation), _Nu4 Hno3_ (Free of anger) & **Anger** \\ \hline
**Surprise** & _Chad YY4_ (Astonished), _Jing1 Ya4_ (Surprise) & / \\ \hline \hline \end{tabular}
\end{table}
Table 5: Map the six emotion categories from the ECPE dataset to the twelve emotion categories in the OCC model. For each emotion mapping, several Emotion-triggering Words are provided as examples, along with the Chinese pinyin of the original words in the dataset and their corresponding English meanings.





\begin{table}
\begin{tabular}{c c l p{142.3pt}} \hline \hline
**Operation** & **Task** & \multicolumn{2}{c}{**Prompt \(p=I||\theta_{1}||\theta_{2}||\cdots||q\)**} \\ \cline{3-5}  & & \multicolumn{2}{c}{**System Instruction \(I\)****User Question \(q\)**} \\ \hline Appraisal & ECE & **System**: “Your task is to extract entities from the text. Given a passage and an associated emotion, identify who in the text is expressing that emotion, and directly provide the person’s name.” & **User**: “Text: [TEXT], who is experiencing the emotion described as [Emotion]? Please answer with the person’s name directly." \\ \hline Cognitive Appraisal & ECE & **System**: “You are a human with emotion cognitive capabilities. Given a passage of text, your task is to appraise the [Desirability] / [Praise/Blame] / [Likelihood] of a particular clause from the perspective of a specified individual.” & **User**: “Text: [TEXT], decide whether clause [c] describes event/action [S] in a way that would be appraised as [Desirable] / [Undesirable] / [Praise/Blame] / [Certain] / [Uncertain] from [p]’s perspective.” \\ \hline Emotion & ECPE & **System**: “You are a human with emotion cognitive capabilities. Given a passage of text, your task is to analyze whether a specific emotion is generated by the appraisal of a particular event or action from the perspective of a specified individual.” & **User**: “According to OCC model, people feel [Emotion] in response to events/actions appraised as [Appraisal]. Given [TEXT], decide whether [p] may feel [Emotion] caused by [C], which describe a [Appraisal] event/action [S].” \\ \hline Cause Analysis & ECE & **System**: “You are a human with emotion cognitive capabilities. Given a passage of text, your task is to analyze the reason for the emotion from the perspective of the specified person.” & **User**: “According to OCC model, people feel [Emotion] in response to events/actions appraised as [Appraisal]. Given this passage, [p] appears clauses like [C] as [Apraisal], leading to the emotion of [Emotion]. Please summarize the specific event/action that caused this emotion.” \\ \hline \hline \end{tabular}
\end{table}
Table 8: Template prompts \(p\) for LLM-based entity extraction in the process of graph construction. \(D\), \(E\) respectively denote the input slots for the Document and the Emotion. For the ECE task, the model extracts the emotion holder from a specified sentence, given the emotion \(E\) and its corresponding clause \(c_{E}\). In the ECPE task, without predefined \(E\) and \(c_{E}\), the model must identify the emotion holders for all emotions within the entire text.