Literary language presents an ongoing challenge for Sentiment Analysis due to its complex, nuanced, and layered form of expression. It is often suggested that effective literary writing is evocative, operating beneath the surface and understating emotional expression. To explore features of implicitness in literary expression, this study takes Ernest Hemingway’s The Old Man and the Sea as a case for examining implicit sentiment expression. We examine sentences where automatic sentiment annotations show substantial divergences from human sentiment annotations, and probe these sentences for distinctive traits. We find that sentences where humans perceived a strong sentiment while models did not are significantly lower in arousal and higher in concreteness than sentences where humans and models were more aligned, suggesting the importance of simplicity and concreteness for implicit sentiment expression in literary prose.

We examine the relationship between the canonization of Danish novels and their textual innovation and influence, taking the Danish Modern Breakthrough era (1870–1900) as a case study. We evaluate whether canonical novels introduced a significant textual novelty in their time, and explore their influence on the overall literary trend of the period. By analyzing the positions of canonical versus non-canonical novels in semantic space, we seek to better understand the link between a novel’s canonical status and its literary impact. Additionally, we examine the overall diversification of Modern Breakthrough novels during this significant period of rising literary readership. We find that canonical novels stand out from both the historical novel genre and non-canonical novels of the period. Our findings on diversification within and across groups indicate that the novels now regarded as canonical served as literary trendsetters of their time.

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.

The literary domain continues to pose a challenge for Sentiment Analysis methods, due to its particularly nuanced and layered nature. This paper explores the adequacy of different Sentiment Analysis tools - from dictionary-based approaches to state-of-the-art Transformers - for capturing valence and modelling sentiment arcs. We take Ernest Hemingway’s novel The Old Man and the Sea as a case study to address challenges inherent to literary language, compare Transformer and rule-based systems’ scores with human annotations, and shed light on the complexities of analyzing sentiment in narrative texts. Finally, we emphasize the potential of model ensembles.

Using a large corpus of English language novels from 1880 to 2000, we compare several textual features associated with literary quality, seeking to examine developments in literary language and narrative complexity through time. We show that while we find a correlation between the features, readability metrics are the only ones that exhibit a steady evolution, indicating that novels become easier to read through the 20th century but not simpler. We discuss the possibility of cultural selection as a factor and compare our findings with a subset of canonical works.