This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Differentiating dreams from wakefulness by automatic content analysis and support vector

Ms. Xiaofang Zheng
Purdue University
Richard Schweickert
Purdue University ~ Psychological Sciences

Dream content is connected to major concerns of the individual’s waking life (e.g., Domhoff & Schneider, 2008a, 2008b). Despite long investigation with laborious content analysis coding, dreams are far from well understood. Automatic quantitative analysis techniques can be not only faster than traditional human hand-coding but also lower in coding errors and bias, and deserve further investigation. Linguistic Inquiry and Word Count (LIWC, Pennebaker, Boyd, Jordan, & Blackburn, 2015) is an automatic technique possibly useful for dream research. We analyzed dream reports and waking life reports of individuals using LIWC and found differences in social content and other aspects. Furthermore, we used a machine learning technique, support vector machines, to detect whether a report described waking life or dreams, based on the LIWC word frequencies of various categories. Automatic content analysis techniques are promising for scientific research on dreams.



content analysis
Linguistic Inquiry and Word Count
linguistic analysis
machine learning
support vector machine

There is nothing here yet. Be the first to create a thread.

Cite this as:

Zheng, X., & Schweickert, R. (2021, July). Differentiating dreams from wakefulness by automatic content analysis and support vector. Paper presented at Virtual MathPsych/ICCM 2021. Via