Investigating the Relationship between Literary Genres and Emotional Plot Development

Evgeny Kim, Sebastian Padó, Roman Klinger


Abstract
Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80%F1); (b), the emotion arc model shows a lower performance (59 % F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 % F1) and an improved performance of an ensemble model (84 % F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).
Anthology ID:
W17-2203
Volume:
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
LaTeCH | WS
SIG:
SIGHUM
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–26
Language:
URL:
https://aclanthology.org/W17-2203
DOI:
10.18653/v1/W17-2203
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W17-2203.pdf