Proceedings of the Advances in Visual Methods for Linguistics (AVML). York, UK.
Year of publication:
The volumes of digitized literary collections in various languages increase at a rapid pace and so increases the need to computationally support the analysis of such data. Literature can be studied in a number of different ways and from many different perspectives and text analysis make up a central component of literature studies. If such analysis can be integrated with advanced visual methods and fed back to the daily work of the literature researcher, then it is likely to reveal the presence of useful and nuanced insights into the complex daily lives, ideas and beliefs of the main characters found in many of the literary works. In this paper we describe the combination of robust text analysis with visual analytics and bring a new set of tools to literary analysis. As a show case, we analyzed a small subset (13 novels of a single author) taken from a large literary collection, the Swedish Literature Bank . The analysis is based upon two levels of inquiry, namely by focusing on mentions of theistic beings (e.g. Gods' names) as well as mentions of persons' names, including their gender and their normalized, linked variant forms, and examining their appearance in sentences, paragraphs and chapters. The case study shows several successful applications of visual analytics methods to various literature problems and demonstrates the advantages of the implementation of visual literature fingerprinting. Our work is inspired by the notion of distant reading or macronalysis for the analyses of literature collections. We start by recognizing all characters in the novels using a mature language technology (named entity recognition) which can be turned into a tool in aid of text analysis in this field. We apply context cues, lists of animacy and gender markers and inspired by the document centered approach and the labelled consistency principle which is a form of on-line learning from documents under processing which looks at unambiguous usages of words or names for assigning annotations in ambiguous words or names. For instance, if in an unambiguous context where there is a strong gender indicator, such as 'Mrs Alexander' the name 'Alexander' is assigned a feminine gender, then subsequent mentions of the same name in the same discourse will be assigned the feminine gender as well unless there is a conflict with another person with the same name. We argue, that the integration of text analysis such as the one briefly outlined and visualization techniques, such as higher resolution pixel-based fingerprinting, could be put to effective use also in literature studies. We also see an opportunity to devise new ways of exploring the large volumes of literary texts being made available through national cultural heritage digitization projects, for instance by exploring the possibility to show several literary texts (novels) at once. We will illustrate some of the applied techniques using several examples from our case study, such as summary plots based on all the characters in these novels as well as fingerprints based on the distribution of characters across the novels.