Examining the taxonomy by [Kim and Klinger 2018b], it becomes immediately evident that the strongest connection between literary theory and SA takes place in the field of narratology. The classification of genres and story types (application 2), in particular, depends primarily on the structure of narratives. [Moretti 2011] has also demonstrated how network analysis (application 4) can prove a powerful approach for the study of plot, while [Zehe et al. 2016] have chosen the narratological device of the “happy ending” to classify their selection of literary texts (application 1). While this does not exclude that SA can also be fruitfully applied to the study of different subjects with different approaches (such as poetry in a neurocognitive perspective, as done by [Papp-Zipernovszky et al. 2021]), the field of narratology and the subject of the novel are clearly the most dominant,[2] with a distinctive interest towards the classification of literary texts (shared by both applications 1 and 2). It is not by chance, then, that Jockers decided to call his SA tool Syuzhet [Jockers 2015a], with a direct reference to the distinction made by Russian formalists between the fabula and the plot (also known as syuzhet, récit, or intreccio).

However, when looking for the actual location where this connection takes place, the model reveals itself as apparently faulty. Both [Jockers 2015b] and [Reagan et al. 2016] used SA for demonstrating that the story arcs of the entire (Western) literary production are dominated by six basic shapes.[3] This conclusion sparkled a lively debate — and not a few criticisms (see [Hammond 2017]) — , but it was based on a very problematic assumption. In fact, these basic shapes were obtained by tracking the evolution of positive vs. negative emotions throughout thousands of stories. A precedent for such an approach was found in a lecture by Kurt Vonnegut, based on a (rejected) Master's thesis and circulated in the form of a mock-academic pièce (now available on YouTube). Some connections in narratology might be found, like in the various proposals for a “generative grammar of narrative” [Prince 1973], which already stimulated applications in the field of artificial intelligence — but just for creative purposes [Bringsjord and Ferrucci 2000] — , or in the structuralist assumption that all stories can be reconducted to a universal model [Bremond 1973] — but just when focusing on the fabula. Antecedents of the concept of story arc can even be found in Gustav Freytag's pyramid of the dramatic action [Freytag 1863] and in Northrop Frye's “U” and an “inverted U” shapes [Frye 1982], representing the archetypical structures of comedy and tragedy. However, when we look into the most established theorizations of narrative form, such as the still-fundamental proposal by [Genette 1972] or the most recent reformulations by [Bal 2017], almost no trace of emotions can be found. Aspects like focalization, space, time, and characters constitute a story through their manipulation and interaction, while affect is nothing but a device used to engage the reader, with just secondary and indirect consequences on the structure of the narrative. In conclusion, the question becomes unavoidable: is the SA approach actually able to develop a formal model of the phenomenon which, traditionally, has been known by literary scholars as “the plot”?

A series of recent studies, produced in the wake of the already-mentioned affective turn, help mitigating a straightforwardly negative answer. Patrick Colm Hogan was the first to introduce the concept of “affective narratology” [Hogan 2011]. His goal was clear and apparently in line with the approaches by Jockers and Reagan: starting from the acknowledgement that “narratological treatments of emotion have on the whole been relatively undeveloped” [Hogan 2011, 1], he aimed at highlighting how “emotion systems define the standard features of all stories, as well as cross-culturally recurring clusters of features in universal genres” [Hogan 2011, 2]. This statement could easily be adopted as an epigraph for many SA literary studies. However, when reaching the core of Hogan's proposal, the correspondences become much more problematic. In particular, Hogan defines a nested system, where works are composed by stories (i.e., by many stories intertwined together), stories by episodes, and episodes by events and incidents, all governed by emotional principles. One of the most important principles in this system is normalcy: in fact, “an episode begins with a shift away from normalcy [and] ends when there is a return to normalcy. […] If normalcy is restored in a more enduring way, we have not just an episode, but a story” [Hogan 2011, 65]. This poses a fundamental problem for computational modeling, because the basic unit of measurement is not uniform (the pages of a book or the words in a sentence, generally taken as reference points by SA tools), but depends on the development of the story itself (episodes can be closed in a few sentences, or develop through multiple pages). In addition, normalcy is not determined by a simple positive vs. negative emotional status, but by more complex emotion systems (e.g., attachment and sexual desire in the romantic genre), which take shape not in the more general context of the story, but with reference to the goals of single characters. The combination of these elements alone makes the emotional analysis of stories much more complex and sophisticated than a simple tracking of the emotional levels through the pages of a book, involving for example the issues of focalization (as the nature of a sentiment inevitably depends on the chosen perspective), style (if we follow its affective-aesthetic interpretation, cf. [Herrmann et al. 2015, 42]), symbolism, and many others.

It should be noted, however, that research in computational literary studies is currently paving the way for solving most of these problems. For example, the identification of scenes in narratives (and thus of Hogan’s episodes) is at the center of the effort by [Gius et al. 2019], who already highlighted its relevance for narratological studies. [Kim et al. 2017] produced their story arcs by distinguishing Plutchik's eight basic emotions (joy, trust, fear, surprise, sadness, anticipation, anger, and disgust) and thus moved beyond the flattening distinction between positive and negative sentiments. Finally, all the studies collected by [Kim and Klinger 2018b] under the “character network analysis” label focus on the emotions of characters. What is currently missing, is a fruitful integration of all these approaches in a coherent framework.

In her recent contribution on the subject, [Elkins 2022] compares the results of more than thirty-six different computational methods in the creation of emotional arcs (a term which she prefers to plot arcs, as it indicates “an underlying sentiment structure that occurs even when very little happens plot-wise” [Elkins 2022, 5]). While strengthening the approach introduced by Jockers and providing multiple arguments to confirm its usefulness in literary studies, Elkins consciously decides not to face the more complex narratological issues described here, thus still leaving unrealized a true operationalization of Hogan's narrative theory.

Hogan's is not the only proposal for an inclusion of affect into narrative science. In her sophisticated contribution, [Breger 2017] takes a distance from Hogan, by distinguishing affect from emotions. Following the Deleuzian interpretation, affect can be intended as an “(asubjective, asymbolic) ‘intensity’” [Breger 2017, 229], which resists any formalization or reduction to universal features. The interesting aspect of Breger's proposal is that the narratological function of affect is consequently limited to the process of worldmaking (the mental creation of fictional worlds), which happens only through an active collaboration between author, text, and readers. While Hogan tried to ground his model uniquely in the inherent features of narratives, excluding — or at least putting aside — the readers, Breger seems to follow a growing tendency in literary studies, which gives a new relevance to readers (be they real or implied). [4]

Such a tendency is also evident in [Oatley 2012], who devises a series of experiments with his own readers. By intermixing the chapters of an original short story with a taxonomy of emotions in literature, Oatley shows how such emotional systems sustain all narratives. A powerful support is found in the Sanskrit theory of rasas, intended as “essences of the everyday emotions […] that seem not so much individual […], but universal: aspects of all humankind” [Oatley 2012, 34].

In a similar effort, [Pirlet and Wirag 2017] refer to Monika Fludernik's theory of a “natural” narratology [Fludernik 1996], which “foregrounds the reader and focuses on the cognitive mechanisms underlying reader's construction and interpretation of narrative” [Pirlet and Wirag 2017, 48]. Fludernik's naturalism depends on the idea that narratives are built in readers' minds through a re-shaping and variation of everyday human experience. By including the affective component, such a narratology may “expand its purview and become even ‘more natural’” [Pirlet and Wirag 2017, 49].

These contributions are just a sample of a currently growing trend in literary studies. The widest body of research on reader's affects and emotions, in fact, can be found in the field of reader response theory, whose origins can be traced back to Aristotle's concept of catharsis [Miall 2018], but which distinguished itself more recently for its “scientific” approach to the study of reading. Through the adoption of empirical methods [Peer et al. 2012], in fact, reader's experiences are analyzed and “measured” via questionnaires and interviews, but also using technologies like eye tracking and fMRI scans. In a recent series of papers, SA also has been introduced in the field.

[Jacobs et al. 2017] adopted SA on Shakespeare's sonnets not to visualize their (frequently improbable) plot arcs, but to measure the “emotion potential” of their verses, with the goal of predicting possible readers' reactions and thus devising new experiments on selected texts. [Pianzola et al. 2020], then, used SA on a social reading platform [Cordón-García et al. 2013], Wattpad, with a goal that connects even more strongly narratology and reader response theory. Given that on Wattpad readers can write comments for each paragraph in a novel (reaching even millions of comments), Jocker's technique was adapted to compare two emotional arcs: that of the text and that of readers' reactions. Results were used to isolate the passages that showed the highest levels of harmony or discrepancy (i.e., where the correlations between the two emotional arcs were the highest or lowest), thus identifying the textual features that support or hinder narrativization.

These studies prove how fruitful the integration between SA tools and literary studies can be. And if, on the one hand, they risk moving too deeply into the realm of social science thus losing contact with literature stricto sensu, on the other hand, they confirm how such a tendency is inscribed in the very practice of distant reading [Underwood 2017]. However, extensive work and reasonable carefulness are still required: main risk is that of an oversimplification of phenomena that necessarily escape any reductionism, while the current results of computational analyses are just scraping their surface. In any case, it seems more and more evident that, while a “theory” for distant reading is still lacking [Ciotti 2018], it is precisely through theoretical reasoning that SA (and other computational methods with it) can actually meet the needs of literary scholars. The possibility of testing proposals like those by Hogan and Oatley can prove extremely valuable for the development of literary studies, whatever the result (a confirmation or a denial) will be. And to the skeptics who — sometimes with good reasons — fear the imposition of quantitative methods over the irreducible subjectivity of literary criticism, it can be answered with [McCarty 2005] that the process of modeling in computational literary studies is never the simple reduction of the phenomenon to a formula, but rather a continuous dynamics between the construction of a model and the confrontation with a reality that always escapes it — the same dynamics that, in the end, sustains any theorization about literature.

When trying to propose a taxonomy of SA tools, at least three main distinctions should be made, based on three interconnected aspects: (1) the emotion theory adopted by the tool (T); (2) the technique used to build the emotion resources (ER); (3) the method adopted to accomplish the analysis (M).

As noted at the beginning, SA approaches in literary studies are frequently many steps behind the most recent advancements in computational linguistics. This depends on the aforementioned intrinsic issues (e.g., the complexity of literary language and the unavailability of annotated corpora), but also on the tendency to adopt already-developed tools, which do not require the expertise of a computer scientist. While, on the one hand, this is a necessity in DH research (which cannot be reduced to a sub-field of computational linguistics), it may also lead to errors and misinterpretations. More subtly, as all tools bring about their implicit theories and biases, any analysis that looks just at the external outcomes without delving into the inner functioning logics, risks unintentionally supporting ideals that are not its own. This is why a critical analysis of SA tools becomes fundamental, focusing at least on the ones that are more extensively used in DH.