The Emotion Dynamics of Literary Novels (2024)

Abstract: Stories are rich in the emotions they exhibit in their narratives and evoke in the readers. The emotional journeys of the various characters within a story are central to their appeal. Computational analysis of the emotions of novels, however, has rarely examined the variation in the emotional trajectories of the different characters within them, instead considering the entire novel to represent a single story arc. In this work, we use character dialogue to distinguish between the emotion arcs of the narration and the various characters. We analyze the emotion arcs of the various characters in a dataset of English literary novels using the framework of Utterance Emotion Dynamics. Our findings show that the narration and the dialogue largely express disparate emotions through the course of a novel, and that the commonalities or differences in the emotional arcs of stories are more accurately captured by those associated with individual characters.

## introduction storytelling is one of the earliest of all human traditions, predating even the invention of writing. emotions are a powerful aspect of storytelling in any form, be it the oral tradition, or with the written word in fictional novels: a compelling story is often one that is able to evoke a strong emotional reaction from its audience. characters (fictional named entities) are often at the center of this emotional connection that readers form with a story; we identify with their struggles, react to their pain, and celebrate their victories. the different characters within a single story usually display varying emotional trajectories through the course of the narrative. this variation is also seen at the level of the novel itself: some are tragedies, some are comedies; some have very dramatic highs and lows, others have a more even emotional tone. kurt vonnegut, in his now-famous lec-ture foot_0 @xcite , spoke about the "shapes of stories", plotting the fluctuations of a character's good or bad fortune on the y-axis as a function of narrative time, from beginning to end, on the x-axis. one can assume that he was referring to the emotional journey of the protagonist of the story in his analysis. prior work in nlp on plotting the shapes of stories, however, has considered a novel as representing a single emotional trajectory, where the dialogue of all the different characters is merged together with the narration to create one overarching novel arc (a simplification made because of the lack of annotated data in which characters are mapped to their utterances -still a challenging nlp problem). in this work, we look instead at the emotion arcs of the individual characters in story, distinguishing them from the arcs of the narration as well as the overall novel arc, and quantify the extent of their variation both within a single novel and across novels written by different authors. our goal is to quantitatively capture longitudinal patterns of a character's emotional states (how emotions change over time), referred to as utterance emotion dynamics (ued) @xcite . through these metrics we examine the following research questions about emotion arcs and emotion change (at an aggregate level) in full-length english novels: the variety in the emotional trajectories of characters within a novel can be quite informative about the type of story it tells. a typical hero-versus-villain story, for example, might display opposing emotional arcs for the two main characters; one that follows the adventures of a group of friends might have more closely-correlated character arcs. 4. effects of gender: are there consistent differences between the emotion arcs of gendered character groups? how does this change with different authors? to answer these questions we conduct experiments that make use of the following resources: our work sheds light on aspects of storytelling that have been under-explored in computational literary studies, and quantitatively demonstrates the importance of centering characters in order to gain a more nuanced understanding of novels. we make our data and code publicly available. 3 2 background and related work ## literary emotion dynamics there are several emotional trajectories of interest given a novel: one can look at the emotions of entire novel text, as has been done in prior nlp work, or consider the narration and dialogue as two text streams of interest, representing the narrator and the characters (we term these text streams as being uttered by meta-speakers). the dialogue can further be analyzed by considering each character's utterances as an individual text stream of interest. we categorize the characters in a novel into three groups based on the volume of their dialogue: major characters are those who contribute at least 10% of the total dialogue in the novel or have at least 100 attributed quotations; minor characters utter fewer than 35 quotations throughout; the rest are labelled intermediate. the temporal ordering of the text in a story can in turn be viewed according to narrative time or chronological time. we order utterances by narrative time, which is of substantial interest for literary analysis as it is the order in which the reader experiences the text uttered by a character foot_3 . we normalize the time for each speaker to lie between the range [0,1], i.e., each speaker starts their emotion state at time point 0 and ends at time point 1, irrespective of the volume of their dialogue. 6 we computed the emotion arc for a speaker using the parameters shown to produce high-quality emotion arcs by @xcite : a rolling window size of 500 words, with the window moving forward by one word at each step until the final window subsumes the final word. in order to compute the emotion state at a particular timepoint (i.e, for a window of words), we use the nrc-vad lexicon (mohammad, 2018a). ## research questions and experiments we now explore a series of research questions (rqs) on the emotional dynamics of literary novels, the diversity of emotion arcs that can be found within a novel, as well as across novels, and how they relate to one another. ## conclusion in this work, we conducted a detailed analysis of the nuances of the emotional trajectories of stories. we first demonstrated that the emotions expressed in the narration of a story are not representative of those expressed by its characters. we then showed that the characters within and across stories can have widely varying emotion arcs, with correlations ranging from highly negative to strongly correlated, and averaging at near-zero correlations. these distinctions highlight the diversity of narrative threads contained in a single novel: different characters go through wildly varying journeys, and these cannot be summarized by a single emotion arc -of the novel, narration, or dialoguealone, or even with a small inventory of prototypical shapes.