# APPReddit: a Corpus of Reddit Posts Annotated for Appraisal

arXiv: https://arxiv.org/abs/2205.15627 · HTML: https://mlsanity.com/paper/2205.15627/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 5 of 5)

- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022)
- [Appraisal Theories for Emotion Classification in Text](https://mlsanity.com/paper/2003.14155/) (2020)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations](https://mlsanity.com/paper/2102.12858/) (2021)

## Built on by (most cited first, 7 of 7)

- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [Automatic Emotion Experiencer Recognition](https://mlsanity.com/paper/2305.16731/) (2023)
- [Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue](https://mlsanity.com/paper/2506.20268/) (2025)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)
- [Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts](https://mlsanity.com/paper/2505.24427/) (2025)

## Abstract (arXiv)

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.