Natural Language Processing for Cognitive Analysis of Emotions

TL;DR

This paper introduces a new French dataset and annotation scheme for emotion analysis in autobiographical texts, addressing limitations of small datasets and sentence-level classification in NLP for emotions.

Contribution

It presents a novel annotation scheme, a French autobiographical dataset, and a rule-based method for emotion and cause annotation, advancing NLP for emotional analysis.

Findings

New French dataset of autobiographical emotional texts

Rule-based approach for emotion and cause annotation

Exploration of graph structures for future emotion analysis

Abstract

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.