Macroanalysis: Digital Methods and Literary History

By Matthew L. Jockers. 2013

560 citations recorded by the source corpus.

Subfields: Literature and Literary Theory

View Paper: https://www.semanticscholar.org/paper/90cdf7bdb7a205693d59ab458ee36bd53bf10f35

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In Literature and Literary Theory

Citations in the corpus

Gender-Based Vocation Identification in Swedish 19th Century Prose Fiction using Linguistic Patterns, NER and CRF Learning (2015, methodology): “…of this work is to use literature as means to expand our understanding of history (Pasco, 2014) by applying macroanalytic techniques (Moretti, 2013; Jockers, 2013) in order to start exploring how women enter literature as characters, which functions do they assume and their working patterns.”
Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels (2017, background): “Automatically learning representations of book plots, as structured summaries of their content, has attracted much attention (cf, Jockers (2013) for a review).”
Applications of Topic Models (2017, methodology): “As an example, Jockers and Mimno [2013] evaluate an intriguing hypothesis, that a topic about religious foundations (Convents and Abbeys) is used more by unknown authors1 than by either (known) male or female authors.”
Significant themes in 19th-century literature (2013, background): “…of 3,346 works of 19th-century British, American, and Irish fiction undertaken in chapter eight Macroanalysis: Digital Methods and Literary History (Jockers 2013).6 Our objective here is to narrow the focus and assess the extent to which author gender is, or is not, a predictor of thematic…”
Story Comprehension for Predicting What Happens Next (2017, background): “, 2015), interpersonal relationships (Chaturvedi, 2016), plotpatterns (Jockers, 2013), narrative structures (Finlayson, 2012).”
Incorporating Structured Commonsense Knowledge in Story Completion (2018, background): “Previous studies on narrative comprehension include character roles identification (VallsVargas, Ontañón, and Zhu 2015), narratives schema construction (Chambers and Jurafsky 2009), and plot pattern identification (Jockers 2013).”
Statistical Mechanics of Optimal Convex Inference in High Dimensions (2016, background): “spanning the sciences, engineering, and humanities, including neuroscience [1,2], systems biology [3], health care [4], economics [5], social science [6], and history [7].”
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships (2016, background): “However, such approaches ignore interactions between characters that lie outside of the established lexicon and cannot account for the dynamic nature of relationships that evolve through the course of a book, such as the vampiric downfall of Lucy and Arthur’s engagement in Dracula (Figure 1) or…”