Unraveling reported dreams with text analytics

TL;DR

This study employs text analytics techniques to differentiate reported dreams from other personal narratives, revealing linguistic markers like uncertainty and scene descriptions that characterize dreams.

Contribution

It introduces a comprehensive analysis combining classification, topic modeling, and coherence analysis to identify linguistic features unique to dream reports.

Findings

Dream texts are distinguishable from other narratives with high accuracy.

Uncertainty markers and scene descriptions are key identifiers of dreams.

Dreams show lower discourse coherence compared to personal stories.

Abstract

We investigate what distinguishes reported dreams from other personal narratives. The continuity hypothesis, stemming from psychological dream analysis work, states that most dreams refer to a person's daily life and personal concerns, similar to other personal narratives such as diary entries. Differences between the two texts may reveal the linguistic markers of dream text, which could be the basis for new dream analysis work and for the automatic detection of dream descriptions. We used three text analytics methods: text classification, topic modeling, and text coherence analysis, and applied these methods to a balanced set of texts representing dreams, diary entries, and other personal stories. We observed that dream texts could be distinguished from other personal narratives nearly perfectly, mostly based on the presence of uncertainty markers and descriptions of scenes. Important markers for non-dream narratives are specific time expressions and conversational expressions. Dream texts also exhibit a lower discourse coherence than other personal narratives.