Unraveling reported dreams with text analytics
This work addresses the need for automated dream analysis and detection, which could benefit psychologists and researchers, but it is incremental as it builds on existing psychological theories like the continuity hypothesis.
The study tackled the problem of distinguishing reported dreams from other personal narratives using text analytics, finding that dream texts could be identified nearly perfectly based on uncertainty markers and scene descriptions, with lower discourse coherence compared to non-dream narratives.
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.