  In the field of dream research, the study of dream content typically relies
on the analysis of verbal reports provided by dreamers upon awakening from
their sleep. This task is classically performed through manual scoring provided
by trained annotators, at a great time expense. While a consistent body of work
suggests that natural language processing (NLP) tools can support the automatic
analysis of dream reports, proposed methods lacked the ability to reason over a
report's full context and required extensive data pre-processing. Furthermore,
in most cases, these methods were not validated against standard manual scoring
approaches. In this work, we address these limitations by adopting large
language models (LLMs) to study and replicate the manual annotation of dream
reports, using a mixture of off-the-shelf and bespoke approaches, with a focus
on references to reports' emotions. Our results show that the off-the-shelf
method achieves a low performance probably in light of inherent linguistic
differences between reports collected in different (groups of) individuals. On
the other hand, the proposed bespoke text classification method achieves a high
performance, which is robust against potential biases. Overall, these
observations indicate that our approach could find application in the analysis
of large dream datasets and may favour reproducibility and comparability of
results across studies.
