Learning Emotion Category Representation to Detect Emotion Relations Across Languages.

Understanding human emotions is crucial for a myriad of applications, from psychological research to advancements in Natural Language Processing (NLP). Traditionally, emotions are categorized into distinct basic groups, which has led to the development of various emotion detection tasks within NLP. However, these tasks typically rely on one-hot vectors to represent emotions, a method that fails to capture the relations between different emotion categories. In this study, we challenge the assumption that emotion categories are mutually exclusive and argue that the connections and boundaries between them are complex and often blurred. To better represent these nuanced interconnections, we introduce an innovative framework as well as two algorithms to learn distributed representations of emotion categories by leveraging soft labels from trained neural network models. For the first time, our approach enables the detection of emotion relations across different languages through an NLP lens, a feat unattainable with traditional one-hot representations. Validation experiments confirm the superior ability of our distributed representation algorithms to articulate these emotional connections. Moreover, application experiments corroborate several interdisciplinary insights into cross-linguistic emotion relations, findings that align with research in psychology and linguistics. This work not only presents a breakthrough in emotion detection but also bridges the gap between computational models and humanistic understanding of emotions.