This paper shows how the dream archive and search engine on DreamBank.net, a Web site containing over 22,000 dream reports, can be used to generate new findings on dream content, some of which raise interesting questions about the relationship between dreaming and various forms of waking thought. It begins with studies that draw dream reports from DreamBank.net for studies of social networks in dreams, and then demonstrates the usefulness of the search engine by employing word strings relating to religious and (...) sexual elements. Examples from two lengthy individual dream series are used to show how the dreams of one person can be studied for characters, activities, and emotions. A final example shows that accurate inferences about a person’s religious beliefs can be made on the basis of reading through dreams retrieved with a few keywords. The overall findings are similar to those in studies using traditional forms of content analysis. (shrink)

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% on the negative emotional tone scale, and for the first time reached an accuracy of 77% on the positive scale. (shrink)