The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.