Word associations contribute to machine learning in automatic scoring of degree of emotional tones in dream reports.
Reza Amini, Catherine Sabourin, Joseph De Koninck
Consciousness and Cognition December 1, 2011 DOI: 10.1016/j.concog.2011.08.003 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 458 |
| Population | Dream reports |
| Key findings | Adding word associations improved the machine learning model's accuracy for scoring negative emotional tone from 59% to 63% and reached 77% accuracy for positive emotional tone. |
Abstract
Scientific study of dreams requires the most objective methods to reliably analyze dream content. In this context, artificial intelligence should prove useful for an automatic and non subjective scoring technique. Past research has utilized word search and emotional affiliation methods, to model and automatically match human judges' scoring of dream report's negative emotional tone. The current study added word associations to improve the model's accuracy. Word associations were established using words' frequency of co-occurrence with their defining words as found in a dictionary and an encyclopedia. It was hypothesized that this addition would facilitate the machine learning model and improve its predictability beyond those of previous models. With a sample of 458 dreams, this model demonstrated an improvement in accuracy from 59% to 63% (kappa=.485) on the negative emotional tone scale, and for the first time reached an accuracy of 77% (kappa=.520) on the positive scale.