Towards a Computational Analysis of Suspense: Detecting Dangerous Situations
This work addresses a gap in computational literary studies for researchers, but it is incremental as it builds on existing annotation and detection methods.
The paper tackles the problem of analyzing suspense in computational literary studies by focusing on detecting dangerous situations and fear in texts, introducing an annotated corpus with 7 danger types and finding that unsupervised methods provide signals but more complex approaches are needed.
Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).