Bad, bad me — of course I’ve been putting off writing up my ideas and thoughts for THATcamp almost to the latest possible moment. Waiting so long has one definitive advantage though: I get to point to some of the interesting suggestions that have already been posted here and (hopefully) add to them.

I’d like to both discuss and do text visualization. Charts, maps, infographics and other forms of visualization are becoming increasingly popular as we are faced with large quantities of textual data from a variety of sources. To linguists and literary scholars, visualizing texts can (among other things) be interesting to uncover things about language as such (corpus linguistics) and about individual texts and their authors (narratology, stylometrics, authorship attribution), while to a wide range of other disciplines the things that can be inferred from visualization (social change, spreading of cultural memes) beyond the text itself can be interesting.

What can we potentially visualize? This may seem to be a naive question, but I believe that only by trying out virtually everything we can think of (distribution of letters, words, word classes, n-grams, paragraphs, …; patterning of narrative strands, structure of dialog, occurrence of specific rhetorical devices; references to places, people, points in time…; emotive expressions, abstract verbs, dream sequences… you name it) can we reach conclusions about what (if anything!) these things might mean.

How can we visualize text? If we consider for a moment how we mostly visualize text today it quickly becomes apparent that there is much more we could be doing. Bar plots, line graphs and pie charts are largely instruments for quantification, yet very often quantitative relations between elements aren’t our only concern when studying text. Word clouds add plasticity, yet they eliminate the sequential patterning of a text and thus do not represent its rhetorical development from beginning to end. Trees and maps are interesting in this regard, but by and large we hardly utilize the full potential of visualization as a form of analysis, for example by using lines, shapes, color (!) and beyond that, movement (video) in a way that suits the kind of data we are dealing with.

What tools can we use to do visualization? I’m very interested in Processing and have played with it, also more extensively with R and NLTK/Python. Tools for rendering data, such as Google Chart Tools, igraph and RGraph are also interesting. Other, non-statistical tools are also an option: free hand drawing tools and web-based services like Many Eyes. Visualization doesn’t need to be restricted to computation/statistics. Stephanie Posavec‘s trees are a dynamic mix of automation and manual annotation and demonstrate that visualizations are rhetorically powerful interpretations themselves.

I hope that some of the abovementioned things connect to other THATcampers’ ideas, e.g. Lincoln Mullen’s post on mining scarce sources and Bill Ferster’s post on teaching using visualization.

Don’t get me started on the potential for teaching. Ultimately translating a text into another form is a unique kind of critical engagement: you’re uncovering, interpreting and making an argument all at once, both to the text in question and to yourself.

Anyway — anything from discussing theoretical issues of visualization to sharing code snippets would fit into this session and I’m looking forward to hearing other campers’ thoughts and experiences on the subject.

I’d like to discuss text mining. I’m currently looking at narratives of conversion offered by fourteen laymen and -women to join a church in East Windsor, Connecticut, in 1700-02. This project raises questions about text mining that I haven’t seen addressed elsewhere. First, how can text mining can help scholars deal with the problem of scarce, rather than abundant, sources? Most projects that I’ve seen use text mining to plow through huge volumes of text. But there are only fourteen narratives from East Windsor (about 30 printed pages), and at most a couple hundred narratives from sixteenth-century New England. How can text mining provide close readings of the scarce documents that scholars from earlier eras work with? Second, how can text mining be adapted to documents that employ a vocabulary that is at once allusive and precise? Nearly every word in these dense narratives is a biblical or theological allusion, which is crucial to their meaning. At the same time, they use a very precise vocabulary. (For example, the term “saving faith” means “the type of faith that saves” rather than the more obvious “faith, which by necessity saves.”) How can text mining bring out the richness of this vocabulary? Though my project is focused on early American religious history, I think the questions it raises could contribute to the larger discussion of text mining.

Since I write for my own blog and for a group blog on the history of American religion, I’d also like to discuss the value and danger of blogging for graduate students and early-career scholars.