“…Although there are many ways to operationally define films' prototypicality or averageness within genres, we focused on simple quantitative measures of semantic (the setting, emotions, and action of a narrative) and syntactic (function word usage within the narrative; Altman, 1984) narrative arc typicality within hand-coded basic-level genres. The use of automated text analysis in the behavioral and computer sciences has rapidly gained momentum in the last decade (Herrmann, van Dalen-Oskam, & Schöch, 2015;Iliev, Dehghani, & Sagi, 2015); however, only a few studies have analyzed narrative or fictional samples with the aim of testing psychological hypotheses (e.g., Berger & Packard, 2018;Danescu-Niculescu-Mizil & Lee, 2011;Iliev, Hoover, Dehghani, & Axelrod, 2016). In the present study, we first analyzed dramatic movie scripts using dictionary-based computerized text analysis and then used profile correlations to assess whether genre-typical arcs in several language dimensions from narrative arc theory (categorization, narrative action, and cognitive processing, as well as negative and positive emotional language; Blackburn, 2015;Malin et al, 2014;Nalabandian et al, 2018) differentially predict audience and critic ratings.…”