Films elicit emotions in viewers by infusing the story they tell with an affective character or tone - in a word, a mood. Considerable effort has been made recently to develop computational methods to estimate affective content in film. However, these efforts have focused almost exclusively on style-based features while neglecting to consider different scene types separately. In this study, we investigated the quantitative determinants of film mood across scenes classified by their setting and use of sounds. We examined whether viewers could assess film mood directly in terms of hedonic tone, energetic arousal, and tense arousal; whether their mood ratings differed by scene type; and how various narrative and stylistic film attributes as well as low- and high-level computational features related to the ratings. We found that the viewers were adept at assessing film mood, that sound-based scene classification brought out differences in the mood ratings, and that the low- and high-level features related to different mood dimensions. The study showed that computational film mood estimation can benefit from scene type classification and the use of both low- and high-level features. We have made our clip assessment and annotation data as well as the extracted computational features publicly available.