task_categories:text-classification language:en size_categories:1K<n<10K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us appraisal emotion
---
configs:
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  data_files:
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    path: data/train-*
dataset_info:
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  - name: postid
    dtype: string
  - name: event_id
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  - name: text
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  - name: Unexpectedness
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  - name: Certainty
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  - name: Consistency
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  - name: Control
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  - name: Responsibility
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  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

## 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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