README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: postid
    dtype: string
  - name: event_id
    dtype: int64
  - name: text
    dtype: string
  - name: Unexpectedness
    dtype: float64
  - name: Certainty
    dtype: float64
  - name: Consistency
    dtype: float64
  - name: Control
    dtype: float64
  - name: Responsibility
    dtype: int64
  splits:
  - name: train
    num_bytes: 248866
    num_examples: 1091
  download_size: 134524
  dataset_size: 248866
task_categories:
- text-classification
language:
- en
tags:
- appraisal
- emotion
size_categories:
- 1K<n<10K
---
# [APPReddit: a Corpus of Reddit Post Annotated for Appraisal](https://arxiv.org/pdf/2205.15627)

## Abstract
> 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.


## Cite this work


> @inproceedings{stranisci2022appreddit,\
>  title={APPReddit: a Corpus of Reddit Posts Annotated for Appraisal},\
>  author={Stranisci, Marco Antonio and Frenda, Simona and Ceccaldi, Eleonora and Basile, Valerio and Damiano, Rossana and Patti, Viviana},\
>  booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},\
>  pages={3809--3818},\
>  year={2022}\
> }

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