Automatic Scoring of Dream Reports' Emotional Content with Large Language Models

Dream research usually depends on human experts manually scoring dream reports, a time-consuming process. While natural language processing tools have been explored for automatic analysis, they could not reason over a report's full context, needed extensive preprocessing, and were rarely validated against manual scoring. This work used large language models, both off-the-shelf and bespoke, to replicate manual annotation of dream reports, focusing on emotions. The off-the-shelf method performed poorly, likely due to linguistic differences across individuals. In contrast, the bespoke text classification method achieved high performance and was robust against biases. This approach may enable analysis of large dream datasets and improve reproducibility and comparability across studies.

Study at a glance

Abstract

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.