Over the last years the automated classification of emotions from text has become an interesting topic in natural language processing with many applications. Theories from psychological studies on emotions have been widely utilized to support the task of the automated assignment of emotions to textual content. Most commonly used theories are the fundamental emotions theory like proposed by Paul Ekman and the dimensional model of affect proposed by Albert Mehrabian and James Russell. However, these theories ignore other psychological theories, namely the cognitive appraisal theories, which explain emotions as a response to an individual interpretation of a given situation. Such appraisal theories have only been minorly used in the attempt to improve performance of emotion classification. In addition, there are no datasets annotated with appraisal dimensions. This work filled this gap by annotating a dataset with appraisal dimensions. Further, this work conducted several experiments in which classification models utilized these appraisal annotations. Although this work was not able to show a clear improvement in a real-world setting, the results show that appraisal dimensions have the potential to improve the performance of classifiers, which predict emotions from text.