Datasets:
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
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}
}