---
title: "Appraisal Theory-Informed Emotion Prediction — Wang 2026"
description: "Most work on Emotion Recognition in Conversation (ERC) assigns a fixed label to each utterance and does not explain why a speaker's feelings change."
authors: ["Xiaowei Wang", "Jayant Teotia", "Rui Mao", "Wandeep Kaur Ratan Singh", "Sabrina Binti Tiun", "Erik Cambria"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.887/"
doi: "10.63317/3rx3wu7m6cgn"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-appraisal-theory-informed-emotion-prediction/"
---
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# Appraisal Theory-Informed Emotion Prediction

**Authors:** [Xiaowei Wang](https://md.research.elamur.ai/free/en/authors/xiaowei-wang/), [Jayant Teotia](https://md.research.elamur.ai/free/en/authors/jayant-teotia/), [Rui Mao](https://md.research.elamur.ai/free/en/authors/rui-mao/), Wandeep Kaur Ratan Singh, Sabrina Binti Tiun, [Erik Cambria](https://md.research.elamur.ai/free/en/authors/erik-cambria/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.887/) · [PDF](https://aclanthology.org/2026.lrec-1.887.pdf) · [DOI 10.63317/3rx3wu7m6cgn](https://doi.org/10.63317/3rx3wu7m6cgn) · [OpenAlex](https://openalex.org/W7160136425)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Most work on Emotion Recognition in Conversation (ERC) assigns a fixed label to each utterance and does not explain why a speaker's feelings change. The authors use Appraisal Theory, which treats emotion as the result of judging whether expectations are met or broken, to define a new task called emotion prediction. Their prompt-based framework splits the reasoning into three steps: inferring the speaker's expectation, detecting a violation of it, and predicting the resulting emotion shift. Six prompting subtasks (Q1–Q6) vary which appraisal cues the model receives. They test eight LLMs on DailyDialog, EmoryNLP, IEMOCAP and MELD and score both emotion labels and transition direction.

Supplying expectation cues gives small but consistent gains. Violation cues alone usually hurt, and combining both cues brings no synergy. Models predict shift direction better than chance but favour "no change" too often. The study offers a cognitively motivated, interpretable benchmark for affective reasoning in LLMs and shows where current models still fall short.

## Key points
- **Prompt conditions:** Q1 builds the speaker's expectation. Q2–Q5 generate the next utterance under four conditions: no cues, expectation, violation, and both. Q6 evaluates the emotional outcome.
- **Accuracy gains:** Expectation prompts raise accuracy by about 1.0% on average and by up to +2.4% on MELD. Q3 beats Q2 in 31/32 configurations on EmoryNLP and MELD but only 17/32 on DailyDialog. Every evaluator ranks the prompts Q3 > Q2 > Q5 > Q4.
- **Model accuracy:** Overall mean accuracy is low, and the top models sit close together: Gemini 2.5 Pro 0.2832, Gemini 1.5 Pro 0.2826, GPT-4o 0.2811. Smaller open models such as Qwen and DeepSeek score about 0.264–0.271.
- **Transition direction:** Agreement on Up/Stable/Down transitions reaches Cohen's κ of about 0.25–0.36, which is fair to moderate and significant under a permutation test.
- **Stability bias:** Models over-predict stable trajectories, most strongly on IEMOCAP (inertia bias above 20%). The bias is weaker or absent on EmoryNLP and DailyDialog.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026appraisal,
  title = {{Appraisal Theory-Informed Emotion Prediction}},
  author = {Wang, Xiaowei and Teotia, Jayant and Mao, Rui and Singh, Wandeep Kaur Ratan and Tiun, Sabrina Binti and Cambria, Erik},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3rx3wu7m6cgn},
  url = {https://doi.org/10.63317/3rx3wu7m6cgn}
}
```

- [← previous Antoni Tronina, Mariusz Szmajdziński, Wprowadzenie do języka syryjskiego (Introduction to Syriac), Studia Biblica 6, Kielce 2003, Instytut Teologii Biblijnej Verbum, 153 pp., ISBN 83-915855-9-X](https://md.research.elamur.ai/free/en/papers/polanski-2026-antoni-tronina-mariusz-szmajdzinski-wprowadzenie/)
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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/)

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