Differentiating dreams from wakefulness by automatic content analysis and support vector

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.