Laura McGrath of Michigan State University (@lbmcgrath) joined us this week via Skype to talk about measuring “literary novelty,” a project she is working on with Devin Higgins and Arend Hintze. The former is MSU’s data librarian, and the latter is a professor at MSU who specializes in computational approaches to genetics and biology. They also collaborated with HathiTrust Research Center. The presentation today focused on introducing us to the collaborative project, then considering how to move into the second stage in the near future. Continue reading Feb 15, 2018 WORD LAB – Laura McGrath (MSU)
Our initial question was, does Underwood and Sellers’s argument stand up? While we were concerned about hand-picking data, one member suggested that “the sampling was sampling” and this was convincing. In addition, it felt that the random sample truly could be as random as possible given that the HathiTrust archives of 19th-century texts is better than other periods. We discussed why that might be: deaccessioning, preservation, simply the sheer numbers of books produced in the 19th century as opposed to earlier, OCR accuracy, and copyright restrictions. Also, there was the procedure of digitization itself — University of Michigan digitized its research collections, for example, rather than things in special collections, and this might have included many more 19th-century books.
We also asked what exactly significance and prestige mean. One member brought up the example of Wendell Harris’s article “Canonicity” and his argument that texts become part of the canon based on being part of the conversation; this would go along with the idea that things being reviewed in magazines are significant in that they are being talked about in the first place, whether positively or negatively. And even if things were being negatively reviewed, they still helped to shape literary conversation and thus “the canon” and what survived over time. One member also raised the question of doing sentiment analysis of some kind (whether yes/no or picking out significant words, as another member suggested) on the reviews and adding that data to the analysis.
A question was also raised by a Wharton member: with literary analysis of this kind, is it more about interpretation or explanation, and what is the outcome of the research? With business research, there would be an action suggested based on the conclusions. We ended up talking about how the paper is finding whether there is something at all to interpret or explain in the first place. The authors stop short of explaining or making generalizations, and emphasize the narrowness of their claims. (Whether one should take this approach or go out on a limb to make bigger, but potentially wrong, claims was also discussed at this point.) We also wondered that if they had success in prediction, does that mean there “is” an explanation somewhere of what makes things significant? Is there a latent pattern that exists, and that we might balk at recognizing, as humans?
Finally, we also discussed why the line was always going upward. It seems that this is because the works reviewed and random non-reviewed sample adhered more closely to the “standards,” whatever they are, over time for some reason. Again, we can speculate but not exactly explain what’s going on behind the scenes there.
One conclusion was that we buy the continuity or lack of change in standards over time. The reason is that we see this in other periods where there is a narrative of change, but in reality, much continuity: Meiji Japan (mid-to-late 19th century), and Lu Shi’s use of classical Chinese in his writing despite being at the vanguard of “modern” Chinese literature.
Addendum: Scott has been tinkering with this and is reducing the list of words and making better predictions, with between 100-400 words. A small subset is always in the list of words. It only looks at the training data to do word selection without seeing test data in advance. Depending on training data it picks slightly different sets but a set of about 15 always appear. The top of the list is “eyes” and if you use just the word “eyes” you get 63% accuracy! See Scott’s Github for the code and results.
We got a future potential WORD LAB project out of this discussion, so it was a very productive reading and session!