While Sentiment Analysis has become increasingly central in computational approaches to literary texts, the literary domain still poses important challenges for the detection of textual sentiment due to its highly complex use of language and devices - from subtle humor to poetic imagery. Furthermore these challenges are only further amplified in low-resource language and domain settings. In this paper we investigate the application and efficacy of different Sentiment Analysis tools on Danish literary texts, using historical fairy tales and religious hymns as our datasets. The scarcity of linguistic resources for Danish and the historical context of the data further compounds the challenges for the tools. We compare human annotations to the continuous valence scores of both transformer- and dictionary-based Sentiment Analysis methods to assess their performance, seeking to understand how distinct methods handle the language of Danish prose and poetry.