Classification of Dreams Using Machine Learning

We describe a project undertaken by an interdisciplinary team of researchers in sleep and in and machine learning. The goal is sentiment extraction from a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of affections. The approach is based on a novel representation, taking into account the leading themes of the dream and the sequential unfolding of associated affective feelings during the dream. The dream representation is based on three combined parts, two of which are automatically produced from the description of the dream. The first part consists of co-occurrence vectors, which ---unlike the standard Bag-of-words model ---capture non-local relationships between meanings of word in a corpus. The second part introduces the dynamic representation that captures the change in affections throughout the progress of the dream. The third part is the self-reported assessment of the dream by the dreamer according to eight given attributes. The three representations are subject to aggressive feature selection. Using an ensemble of classifiers and the combined 3-partite representation, we have achieved 64% accuracy, which is in the range of human experts' consensus in that domain.