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.
翻译:尽管用于情感认知的计算资源很多,但缺乏依赖评估模型的数据集。根据评估理论,情感是对事件进行多层面评估的结果。在本文中,我们提出APPReddit,这是第一个根据这一理论附加说明的非实验性数据集。在描述其发展后,我们将我们的资源与ENIEAR进行比较,这是在实验环境中创建的一系列事件,并附有评估说明。结果显示,尽管数据类型和说明计划不同,但可以对这两个公司进行测绘。关于APPReddit的SVM模型预测了四个评估层面,但没有重大损失。在一次培训中将两个公司合并,增加了4个层面中的3个的预测。这些发现为更好地执行评估预测的分类模型铺平了道路。