E motion A rcs: Emotion Arcs for 9,000 Literary Texts (2024)

Abstract: We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English novels, assembled to understand the dynamics of emotions represented in text and how these emotions may influence a novel ́s reception and perceived quality. We evaluate emotion arcs manually, by comparing them to human annotation and against other similar emotion modeling systems to show that our system produces coherent emotion arcs that correspond to human interpretation. We present and make this resource available for further studies of a large collection of emotion arcs and present one application, exploring these arcs for modeling reader appreciation. Using information-theoretic measures to analyze the impact of emotions on literary quality, we find that emotional entropy, as well as the skewness and steepness of emotion arcs correlate with two proxies of literary reception. Our findings may offer insights into how quality assessments relate to emotional complexity and could help with the study of affect in literary novels.

## introduction sentiment analysis and emotion detection are subjective in nature, as not even humans can typically agree on which emotions any specific text contains @xcite . there are also crucial distinctions between whether we are measuring the evocation or association of emotions and whether we are doing this from the reader's or the writer's perspective @xcite . approaches to sentiment analysis garner critique both for inherent problems in, for example, word-based annotation @xcite , but also for being overly focused on evaluation metrics over applicability to downstream tasks @xcite and how the task of emotion detection to † these authors contributed equally to this work some degree constructs the phenomena it is trying to measure @xcite . the importance of a literary text's emotional profile for its overall quality ("performance", reception) is hard to overestimate @xcite . while literary narratives are far from being only matters of emotions, the emotions touched upon in texts -in both explicit and evocative ways -determine essential aspects of the reader's experience at the structural and stylistic level @xcite . however, while this relation between emotions in literary texts and reader experience can seem relatively intuitive, it needs to be more obvious to test or quantify. this presents us with a few difficulties. the first difficulty is the modeling of "emotions in the text" -defining what we mean by that, deciding which emotions to define, and how to measure the emotional content of any given textual unitword, phrase, sentence, or paragraph. due to the complexity of human readers' interpretations and experiences of texts, this is a difficult task to model. the second difficulty is quantifying the relation between emotions in text and their reception or perceived quality of a literary narrative. in this paper, we introduce a new resource, 'emotionarcs', to explore the relationship between these emotion arcs and literary quality complete with some early analyses. 'emotionarcs', is a dataset that comprises emotional arcs constructed from over 9,000 english novels through a novel approach that utilizes emotion intensity lexicons enhanced by word embeddings fine-tuned for the domain of literature to construct emotion arcs. we use the dataset to analyze and measure how affective language impacts a novel's literary quality, measured both through library holding numbers and goodreads ratings. ## related work computational literary studies (cls) is an active field of research affiliated with digital humanities and applied natural language processing. sentiments, emotions, and affect are all common research topics within cls and include work in emotion classification, genre classification, story-type clustering, sentiment tracking, and character analysis @xcite . ## agreement and validation as the approach used in this project does not allow for traditional accuracy measures often used in machine learning @xcite , we focus our validation efforts on comparing human interpretations with those generated by our lexicon-based model, which has shown to be accurate in multiple prior studies @xcite @xcite . we validated the emotionarcs resource in three different ways: ## concluding discussion with 'emotionarcs' we have presented a new resource for the study of emotions in literary novels that we hope will enable many other researchers to investigate how affect in literary works is intertwined with other aspects of literature. we have shown that our method produces reliable, useful, and easily interpretable emotion arcs that can help more traditional literary scholars compare larger corpora of literary works that are possible using only qualitative methods. it seems that overall emotional entropy, the slopes of emotion arcs, and their level of skewness hold some relation with the reception of the novels as measured via rating count and library holdings.