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.

通过开发一个按照多维评估理论进行注释的非实验数据语料库APPReddit，我们证明了SVM模型可以预测4个评估维度而不会失去重要信息，并且将APPReddit和enISEAR数据集合并以进行训练可以提高评估维度预测的准确性，这为评估预测的更好分类模型铺平了道路。

APPReddit: 一个Reddit帖子语料库，用于评估标注