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Reader reviews of literary fiction on social media, especially those inpersistent, dedicated forums, create and are in turn driven by underlyingnarrative frameworks. In their comments about a novel, readers generallyinclude only a subset of characters and their relationships, thus offering alimited perspective on that work. Yet in aggregate, these reviews capture anunderlying narrative framework comprised of different actants (people, places,things), their roles, and interactions that we label the "consensus narrativeframework". We represent this framework in the form of an actant-relationshipstory graph. Extracting this graph is a challenging computational problem,which we pose as a latent graphical model estimation problem. Posts and reviewsare viewed as samples of sub graphs/networks of the hidden narrative framework.Inspired by the qualitative narrative theory of Greimas, we formulate agraphical generative Machine Learning (ML) model where nodes represent actants,and multi-edges and self-loops among nodes capture context-specificrelationships. We develop a pipeline of interlocking automated methods toextract key actants and their relationships, and apply it to thousands ofreviews and comments posted on Goodreads.com. We manually derive the groundtruth narrative framework from SparkNotes, and then use word embedding tools tocompare relationships in ground truth networks with our extracted networks. Wefind that our automated methodology generates highly accurate consensusnarrative frameworks: for our four target novels, with approximately 2900reviews per novel, we report average coverage/recall of important relationshipsof > 80% and an average edge detection rate of >89\%. These extracted narrativeframeworks can generate insight into how people (or classes of people) read andhow they recount what they have read to others.

An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com