  Emotion classification in text is a challenging task due to the processes
involved when interpreting a textual description of a potential emotion
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) than only those
that represent basic categories as they have been proposed by Paul Ekman
(anger, disgust, fear, joy, surprise, sadness). 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.
