  This paper describes submissions from the team Nostra Domina to the EvaLatin
2024 shared task of emotion polarity detection. Given the low-resource
environment of Latin and the complexity of sentiment in rhetorical genres like
poetry, we augmented the available data through automatic polarity annotation.
We present two methods for doing so on the basis of the $k$-means algorithm,
and we employ a variety of Latin large language models (LLMs) in a neural
architecture to better capture the underlying contextual sentiment
representations. Our best approach achieved the second highest macro-averaged
Macro-$F_1$ score on the shared task's test set.
