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We explore the representational space of emotions by combining methods fromdifferent academic fields. Cognitive science has proposed appraisal theory as aview on human emotion with previous research showing how human-rated abstractevent features can predict fine-grained emotions and capture the similarityspace of neural patterns in mentalizing brain regions. At the same time,natural language processing (NLP) has demonstrated how transfer and multitasklearning can be used to cope with scarcity of annotated data for text modeling.  The contribution of this work is to show that appraisal theory can becombined with NLP for mutual benefit. First, fine-grained emotion predictioncan be improved to human-level performance by using NLP representations inaddition to appraisal features. Second, using the appraisal features asauxiliary targets during training can improve predictions even when only textis available as input. Third, we obtain a representation with a similaritymatrix that better correlates with the neural activity across regions. Bestresults are achieved when the model is trained to simultaneously predictappraisals, emotions and emojis using a shared representation.  While these results are preliminary, the integration of cognitiveneuroscience and NLP techniques opens up an interesting direction for futureresearch.

A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions