WordSeer: A Text Analysis Environment for Literature Study

This project will continue on the success of a Digital Humanities Startup grant (HD-51244-11) to produce a software environment for literary text analysis. Literature study is a cycle of reading, interpretation, exploration, and understanding. Called WordSeer, this software system integrates tools for automated processing of text with interaction techniques that support the interpretive, exploratory, and note-taking aspects of scholarship. Development of the tool follows best practices surrounding user-centered design and evaluation. At present, the system supports grammatical search and contextual similarity determination, visualization of patterns of word context. This implementation grant will allow for incorporating additional tools to aid comparison, exploration, grouping, and hypothesis formation, and to make the software more robust and therefore sharable and usable by a wide community of scholars

A Text Analysis Tool for Examining Stylistic Similarities in Narrative Collections

Increasing numbers of primary and secondary source texts have been digitized in recent years. Scholars who want to study these new collections in depth need computational assistance because of their large scale. The non-programmer tools for text analysis currently available operate at the word level, and they show tables of counts and lists of occurrences, but rarely interactive visualizations. We propose to build a text analysis tool that includes visualizations and works on the grammatical structure and stylistic features of text, applying highly accurate technology from computational linguistics and authorship identification to extract this information. We will develop our tool for a collection of slave narratives whose authorship is ambiguous. In doing so, we will find out whether visualizations of grammatical and stylistic features are useful to literary scholars, and whether this information allows them to make satisfying large-scale analyses of their text

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field