  Text classification tasks have improved substantially during the last years
by the usage of transformers. However, the majority of researches focus on
prose texts, with poetry receiving less attention, specially for Spanish
language. In this paper, we propose a semi-supervised learning approach for
inferring 21 psychological categories evoked by a corpus of 4572 sonnets, along
with 10 affective and lexico-semantic multiclass ones. The subset of poems used
for training an evaluation includes 270 sonnets. With our approach, we achieve
an AUC beyond 0.7 for 76% of the psychological categories, and an AUC over 0.65
for 60% on the multiclass ones. The sonnets are modelled using transformers,
through sentence embeddings, along with lexico-semantic and affective features,
obtained by using external lexicons. Consequently, we see that this approach
provides an AUC increase of up to 0.12, as opposed to using transformers alone.
