Mrs. Dalloway Said She Would Segment the Chapters Herself (2023)

Abstract: This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf’s Mrs. Dalloway, a novel widely considered to be “plotless. Combining transformer-based sentiment analysis models with statistical testing, we model sentiment’s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.

## introduction what is the shape of a story? narratologists have long been fascinated with reducing narratives to a compelling linear visual rhetoric: the narrative arc, a line chart that smoothly demonstrates the (emotional) rise and fall of the story @xcite @xcite . recent scholarship has introduced emotive expressions and affect as a vital analytical tool for the construction of such narrative arcs @xcite @xcite . the digital humanities community has shown great interest in operationalizing this problem as a sentiment analysis task across various literary corpora (jockers 2015; @xcite . the success of this approach has recently been extended beyond the literary domain to encompass a wider range of inquiries driven by social science @xcite . meanwhile, existing methods for sentimentbased narrative arc extraction tend to underperform on what literary scholars call non-linear narratives @xcite . we posit that literary works often assume varying degrees of clarity and straightforwardness when conveying a story, an explicative quality known as narrativitycomputationally, it has been defined as a scalar measuring the success of a work in conveying a linear sequence of events as narrative discourse @xcite . while some novels may convey their story-worlds with relative transparency via chronological accounts of their fictional agents' actions, others may withhold it from the audience for artistic purposes @xcite . this nonlinearity has been considered a hard problem for narratology, by both computational @xcite and traditional @xcite approaches. virginia woolf's 1925 novel mrs. dalloway, in particular, has been identified as an especially recalcitrant text to model with existing methods @xcite , possibly due to its renowned stream-ofconsciousness style. this study takes on the challenging task of performing unsupervised plot extraction on mrs. dalloway, a novel widely held and celebrated by literary scholars to be essentially "plotless." we hypothesize that it is possible to excavate latent plot structures from nonlinear fiction if we use sentiment data to statistically model the notion of non-linearity itself. to avoid the pitfall of imposing linear narrative arcs onto non-linear narratives via smoothing-based de-noising techniques, we propose a sentiment-centric pipeline which instead aims to embrace the "noise" inherent to a nonlinear and highly fragmented novel like mrs. dalloway. the goal of this pipeline is to capture the full expression of non-linearity in sentiment data. leveraging the softmax probability distributions of pre-trained language models like bert @xcite and electra @xcite , we perform a paired t-test that models sentiment's rateof-change to identify breakpoints and correspondingly segment the novel into emotionally coherent parts. our approach finds 19 such surrogate "chapters" in mrs. dalloway, which we then qualitatively evaluate to assess their literary and narratological coherence. to further verify the validity of our results, we quantitatively evaluate our pipeline on a linear fiction dataset to determine its ability to restore existing chapter structures, while also contributing a generative approach to the task of text segmentation in the literary domain. ## methods this section describes the experimental designs of our pipeline. it takes a text file of mrs. dalloway and outputs an unsmoothed narrative arc segmented into surrogate "chapters," as shown in figure 1 below. ## results and discussion to holistically evaluate the validity and limitations of our pipeline's output on mrs. dalloway shown in figure 1 , we follow the approach of @xcite to present the outcomes for literary close reading alongside quantitative metrics. ## conclusion and future work with a pipeline capable of excavating nonlinear plot from both non-linear and linear novels, this study takes the first steps to 1) investigate the the qualitative analysis in section 4.1 shows that the general domain bert model produces more explainable results than the electra model fine-tuned on victorian novels, while electra quantitatively outperforms bert in section 4.2. this is not so much a contradiction as a guidance for future research: perhaps the "literary domain" is not a monolith, but an umbrella term for a collection of domains that are significantly different from each other. is the domain barrier between linear and non-linear fiction? if so, then electra could be considered as an "in-domain" model for experiments in 4.2 because the object of inference is linear fiction, while not for 4.1 since it concerns non-linearity. it is possible that if electra is fine-tuned on a non-linear fiction dataset with sentiment labels, it could further improve upon the findings of 4.1. aside from these questions, other potential directions for our future work include 1) designing more robust methods for quantifying non-linearity in fiction, which could be leveraged for a wide range of inquiries in digital humanities, 2) combining our sentiment-based pipeline with existing semantic-based approach to improve the performance of chapter segmentation, and 3) expanding this research to narratives beyond the literary domain, which is also a key interest of contemporary narratology. we also hope to open up the discussion of non-linearity in fictional narratives to event-centric and character-centric approaches to better understand the interplay between causal and emotional dimensions of plot.