Natural Language Processing for Cognitive Analysis of Emotions
This work addresses emotion analysis for cognitive coaching applications, but it is incremental as it builds on existing methods with a new dataset and annotation scheme.
The authors tackled the limitations of small, homogeneous datasets and simplified sentence-level classification in emotion analysis by introducing a new annotation scheme and a French dataset of autobiographical emotional accounts, resulting in a rule-based approach for automatically annotating emotions and their causes.
Emotion analysis in texts suffers from two major limitations: annotated gold-standard corpora are mostly small and homogeneous, and emotion identification is often simplified as a sentence-level classification problem. To address these issues, we introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset composed of autobiographical accounts of an emotional scene. The texts were collected by applying the Cognitive Analysis of Emotions developed by A. Finkel to help people improve on their emotion management. The method requires the manual analysis of an emotional event by a coach trained in Cognitive Analysis. We present a rule-based approach to automatically annotate emotions and their semantic roles (e.g. emotion causes) to facilitate the identification of relevant aspects by the coach. We investigate future directions for emotion analysis using graph structures.