Abstract
Emotions have attracted a lot of attention in psychology, socio- and psycholinguistics and communication science, but since the past decade also in the fields of computational linguistics and natural language processing. In the latter fields, the term emotion detection is used to refer to the task of automatically identifying fine-grained emotions in texts. Research on emotion detection has mainly focused on English, but with the emergence of (multilingual) large language models, the interest in multilingual approaches to emotion detection increased.
Meanwhile, state-of-the-art research in psychology have developed new theories about emotion, claiming that emotions are not universal: neither in conceptualization, nor in emotion expression. This might have consequences for how multilingual emotion detection models work.
Therefore, it is crucial to investigate differences in emotional language use across languages. Most studies that deal with the cultural component of emotions are limited to studying the translatability of emotion words, or focus on very specific cases and language pairs. In this research project, we will transcend the word level and go beyond the comparison of language pairs by comparing emotion verbalization across ten languages, using methods from computational linguistics. Moreover, we will investigate how state-of-the-art emotion detection models deal with cross-lingual differences in emotion verbalization.
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