Emotion classification in text is a challenging and subjective task, due to
the involved cognitive inference processes that are required to interpret a
textual stimulus. In addition, the set of emotion categories is highly
domain-specific. For instance, literature analysis might require the use of
aesthetic emotions (e.g., finding something beautiful), and social media
analysis could benefit from fine-grained sets (e.g., separating anger from
annoyance) in contrast to basic emotion categories. This renders the task an
interesting field for zero-shot classifications, in which the label set is not
known at model development time. Unfortunately, most resources for emotion
analysis are English, and therefore, most studies on emotion analysis have been
performed in English, including those that involve prompting language models
for text labels. This leaves us with a research gap that we address in this
paper: In which language should we prompt for emotion labels on non-English
texts? This is particularly of interest when we have access to a multilingual
large language model, because we could request labels with English prompts even
for non-English data. Our experiments with natural language inference-based
language models show that it is consistently better to use English prompts even
if the data is in a different language.

情感分类是一个具有挑战性和主观性的任务，需要解释文本刺激所需的认知推理过程。本文针对存在的研究空白，探讨了在非英语文本中使用哪种语言进行情感标签提示的问题，并通过自然语言推理基于语言模型的实验结果表明，即使数据来源是不同语言，使用英语提示更为有效。