The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences

Dream reports—oral or written accounts given upon awakening—are a key source of insight into dreaming. Traditionally, structural information (e.g., word or sentence counts) and semantic content (scored by human coders using predefined systems) are extracted from these reports. This review argues that linguistic analysis methods—graph analysis, dictionary-based content analysis, and distributional semantics—can complement or replace classical measures. These techniques allow direct, operator-independent extraction of quantitative information, enabling fully objective and reproducible analysis of conscious experiences during sleep. They can be partially or fully automated, making them suitable for large datasets.

Study at a glance

Abstract

The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Importantly, one of the main sources of insight into dreaming activity are the (oral or written) reports provided by dreamers upon awakening from their sleep. Classically, two main types of information are commonly extracted from dream reports: structural and semantic, content-related information. Extracted structural information is typically limited to the simple count of words or sentences in a report. Instead, content analysis usually relies on quantitative scores assigned by two or more (blind) human operators through the use of predefined coding systems. Within this review, we will show that methods borrowed from the field of linguistic analysis, such as graph analysis, dictionary-based content analysis, and distributional semantics approaches, could be used to complement and, in many cases, replace classical measures and scales for the quantitative structural and semantic assessment of dream reports. Importantly, these methods allow the direct (operator-independent) extraction of quantitative information from language data, hence enabling a fully objective and reproducible analysis of conscious experiences occurring during human sleep. Most importantly, these approaches can be partially or fully automatized and may thus be easily applied to the analysis of large datasets.