This directory contains 450 novels that appeared between 1770 and 1930 in German, French and English. It is designed for us in teaching and research.
Andrew Piper mentioned a corpus that he put together, txtlab Multilingual Novels. This corpus is of some 450 novels from the late 18th century to the early 20th (1920s). It has a gender mix and is not only English novels. This corpus was supported by SSHRC through the Text Mining the Novel project.
On Thursday and Friday (Oct. 22nd and 23rd) I was at the 2nd workshop for the Text Mining the Novel project. My conference notes are here Text Mining The Novel 2015. We had a number of great papers on the issue of genre (this year’s topic.) Here are some general reflections:
- The obvious weakness of text mining is that it operates on the novel as text, specifically digital text (or string.) We need to find ways to also study the novel as material object (thing), as a social object, as a performance (of the reader), and as an economic object in a market place. Then we also have to find ways to connect these.
- So many analytical and mining processes depend on bags of words from dictionaries to topics. Is this a problem or a limitation? Can we try to abstract characters, plot, or argument.
- I was interested in the philosophical discussions around the epistemological in novels and philosophical claims about language and literature.
This week SSHRC announced the new partnership grants awarded including one I am a co-investigator on, NovelTM: Text Mining the Novel.
This project brings together researchers and partners from 21 different academic and non-academic institutions to produce the first large-scale quantitative history of the novel. Our aim is to bring new computational approaches in the field of text mining to the study of literature as well as bring the unique knowledge of literary studies to bear on larger debates about data mining and the place of information technology within society.
NovelTM is led by Andrew Piper at McGill University. At the University of Alberta I will be gathering a team that will share the resulting computing methods through TAPoR and developing recipes or tutorials so that others can try them.
Tyler Trkowski has written a Feature for NOISEY (Music by Vice) on Rap Game Riff Raff Textual Analysis. It is a neat example of text analysis outside the academy. He used Voyant and Many Eyes to analyze Riff Raff’s lyrical canon. (Riff Raff, or Horst Christian Simco, is an eccentric rapper.) What is neat is that they embedded a Voyant word cloud right into their essay along with Word Trees from Many Eyes. Riff Raff apparently “might” like “diamonds” and “versace”.
The Tri-Council Agencies (Research councils of Canada) and selected other institutions (going under the rubric TC3+) have released an important Consultation Document titled Capitalizing on Big Data: Toward a Policy Framework for Advancing Digital Scholarship in Canada. You can see a summary blog entry from the CommerceLab, How big data is reshaping the future of digital scholarship in Canada. The document suggest that we have many of the components of a “well-functioning digital infrastructure ecosystem for research and innovation”, but that these are not coordinated and Canada is not keeping up. They propose three initiatives:
- Establishing a Culture of Stewardship
- Coordination of Stakeholder Engagement
- Developing Capacity and Future Funding Parameters
The first initiative is about research data management and something we have been working on the digital humanities for some time. It is great to see a call from our funding agencies.
We are finally getting results in a long slow process of trying to study tool discourse in the digital humanities. Amy Dyrbe and Ryan Chartier are building a corpus of discourse around tools that includes tool reviews, articles about what people are doing with tools, web pages about tools and so on. We took the first coherent chunk and Ryan has been analyzing it with R. The graph above shows which years have the most characters. My hypothesis was that tool reviews and discourse dropped off in the 1990s as the web became more important. This seems to be wrong.
Here are the high-frequency words (with stop words removed). Note the modal verbs “can”, “will”, and “may.” They indicate the potentiality of tools.
“ii” 1514 (Not sure why)
Tim sent me a link to another news story on the Criminal Intent project that I am part of. This one is in Science News and is titled, Crime’s Digital Past. The article in by Bruce Bower and dated July 30th, 2011 (which, I know, is in the future.) One of the better stories.
Google has release a neat new tool that uses their Google Books database. The Google Ngram Viewer lets you plot the relative frequencies of words and phrases over time.
Information about the tool can be found at, http://ngrams.googlelabs.com/info.
The graph above shows truth (blue) graphed against false (red).
The CIRCA Histories and Archives group I am part of is organizing the University of Alberta’s first Digitization Day.
This one-day event is a chance for research projects that are digitizing evidence to meet up with each other and with units on campus that provide relevant research services. Projects that are creating digital archives of different sorts will give short presentations as will units on campus that support research.
The idea is to bring a lot of digitization projects together to learn about each other and what is happening on campus. My sense is that we have hit a critical mass on campus and now that we have a trusted digital repository ERA (Education and Research Archive) it is time to start talking and sharing knowledge. Each project should not have to reinvent itself.
The TAPoR Portal has moved to a new server at the University of Alberta. The new location will allow us here to start redesigning it and developing version 2.0. (Or is it now version 3.0?) I underestimated how much work it is to move something so complex. We had to work on bugs, we had to warn users, we had to set up hardware here. Kamal Ranaweera worked very hard to do this – Bravo!
Some links related to the move: