In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges assigned a score to describe dream sentiments. We ran four baseline algorithms in an attempt to automate the rating of sentiments in dreams. Particularly, we compared the General Inquirer (GI) tool, the Linguistic Inquiry and Word Count (LIWC), a weighted version of the GI lexicon and of the HM lexicon and a standard bag-of-words. We show that machine learning allows automating the human judgment with accuracy superior to majority class choice
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