Dream content analysis using Artificial Intelligence
Patrick Mcnamara, Kelly Duffy-Deno, Tom Marsh, Thomas Jr. Marsh
University Library Heidelberg June 26, 2018 DOI: 10.11588/ijodr.2019.1.48744 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 635 |
| Population | Users of Dreamboard.com who posted dreams |
| Duration | 4 years |
| Topics | Dreaming |
| Keywords | Dream Content measure theory Negative mood Content analysis Artificial intelligence Social science Psychotherapist |
| Citations | 11 |
| Key findings | An AI algorithm trained on over 35,000 dreams identified 47 dream themes and found small but significant male-female differences in 34 themes, with female dreams showing higher incidence for most. |
Abstract
We developed a dream content analysis system (DCAS) based on an artificial intelligence (AI) algorithm that was trained using a relatively large corpus of over 35,000 dreams. This sample of dreams were supplied by 424 female and 211 male users over 4 years who had posted them at the dream posting website and app Dreamboard.com. Building upon previous dream content ontologies developed by Hall, Van de Castle, Domhoff and Bulkeley, forty-seven reliably identified dream themes emerged from repeated application of algorithm and agent training procedures. DCAS reproduced most of the key dream content themes from these previous ontologies but also returned some unexpected findings. Mixed-model estimation detected significant male-female content differences for 34 dream themes, with female dreams evidencing higher incidence percentages for most themes, but effect sizes were small. Mixed-model logistic regression identified those themes that best predicted self-reported positive or negative mood associated with dreams. We conclude that the AI-based DCAS algorithm developed here is a promising tool for detailed analyses of dream content patterns.