What does an effective interface for studying stylistic adjective use in a text look like?
Interfaces for text exploration are an active area of inquiry within human-computer interaction. Laypeople want to browse personal documents, journalists and decision makers want detailed news media analytics, and lately, scholars in the humanities want to analyze recently digitized sources. We will first describe the most closely related work in the digital humanities, and then describe motivating literature from natural language processing and human-computer interaction.
In the digital humanities, the closest work to our project comes from two well-known text analytics efforts. The first is the MONK project  incorporating the SEASR analysis toolkits , a collaborative effort from various universities and institutions in the United States and Canada, supported by the Mellon foundation. These projects offers two computational linguistics tools in addition to word distribution and frequency statistics: tagging words with their parts of speech and extracting named entities. Users can visualize occurrence patterns of word sequences within a chosen text, and plot networks of how often named entities occur near each other. A subset of these researchers, some from the HCI Lab at UMD, produced visual text-mining analyses of Emily Dickinson’s correspondence , and of Gertrude Stein’s “The Making of Americans”, and an interface for exploring the parts of speech used near query words of interest .
The second is Voyeur, a project from Stefan Sinclair’s group at McMaster University . Voyeur operates entirely at the word level. It allows users to plot word frequencies, see concordances (contexts in which words occur) and create tag clouds.
Other digital humanities projects have used more advanced language processing, but have not developed them into user interfaces or combined them with visualizations. At the recently formed Humanities Computing Lab at Stanford University, topic modeling is being applied to 19th Century British and American novels . These novels were also the subjects of cutting-edge computational linguistics research at Columbia University that showed how to automatically extract social networks from free text . At the university of Washington, researchers are applying topic modeling to the compendium of Danish, Norwegian, and Swedish folklore collected by Evald Tang Kristensen, and have won an award to continue analysis on Google Books’ Scandinavian collection. In the field of visualization, applications to text in the humanities have been limited to word clouds, and node-and-link diagrams of named entities, and co-occurrences.
Outside the realm of text, but in the domain of comparative exploration, LISA, a comparison search interface for cultural heritage artifacts was created by Amin et. al. .
The digital humanities work described above comes from the application of ideas from human-computer interaction and natural language processing. From human-computer interaction, we are informed by general principles of search user interface design described by Hearst , especially those about visualizing linguistic information, and of visual exploration of large data-sets described by Shneiderman . The idea of visually representing text as a block of tiles goes as far back as 1995, with Hearst using it to show the distribution of search terms in retrieved documents .
Interfaces for exploring the grammatical relationships between words is a newer area of research, probably due to the fact that natural language parsers have only recently become fast enough to process large amounts of text in a reasonable time span. In this sub-field, we are only aware of the work of van Ham, Wattenberg and Viegas on Phrase Nets , which we adapt for comparing narratives side-by-side.
In natural language processing, there are two areas of research that are related to our work: sentiment analysis, and meme tracking, which aim to give users a feeling for the contents of a large body of text. Sentiment analysis involves extracting relevant features about an item of interest from in product reviews, news articles, and other streams of text, and categorizing the language used to describe them as positive or negative. State of the art methods by Popescu and Etzioni  use pattern-based information extraction  and dependency parsing to extract adjectives that apply to e.g. various features of consumer products.
Meme tracking involves either modeling text as a bag-of-words generated by topics that vary over time (an approach due to Blei and Lafferty) , or by tracking the distribution of variations on popular phrases , used by Leskovec et. al.
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