3 min read
While I was able to learn about the number of books published in different years and to ascertain a little bit about the prevalence of different narrative forms in the 18th century, I felt limited in what I could do with this dataset using Fusion Tables. First of all, I found it very difficult to draw conclusions about changes over time because, as the writeup mentions, you can only see the count of books that meet a certain criteria rather than the percentage of books published at a given time that meet that criteria. So when I went to see how the use of epigraphs trended over time, I couldn’t discount the possibility that the trends I saw were simply because the overall count of novels increased (especially considering that the epigraph trend very nearly mimicked the count trend). I think Voyant is much more useful for those kinds of inquiries.
I also think that it would have been interesting to see the trend in authors’ use of prefaces — which we would expect to decrease as the public grew more comfortable with fictionality— but the “paratext titles” were all grouped together for each work, so you couldn’t separate the table of contents from the preface. This was the same for title adjectives — I found the category very interesting, but couldn’t find out much about them because while it’s likely that two titles will share one adjective, It’s unlikely they’ll share all 4 or 5 of their title adjectives. However, using a network graph, I got somewhat of a sense of the usage for each narrative form. If the data was separated for each title adj., say in a set that compared only the title adjective with the narrative form of the work, I think this tool would be very interesting. You could see which terms were shared by epistolary works and other first-person works as well as which ones were not shared, etc. The map was also interesting because I did not know that so many works were published outside of London.
I thought the word cloud that I made was the most interesting finding using this dataset. I chose to look at title adjectives, which I think is a great parameter for a dataset of novels. Again, I am missing Voyant’s capability to clean words from the word cloud, but I thought it was interesting how much the titles attempted to sell the novel, and in what way. Apparently, making sure the reader knows your protagonist is young and female was important; the word "male" did not show up as often and "old" certainly didn't show up. I also think it's funny that "moral" and "historical" competed with "interesting" and "entertaining" (entertaining is the winner, in terms of usage). The word "secret" was also popular -- here we see the interest in the private workings of the young female mind, I guess. I would definitely like to see historical trends on which of these words continued to be used; I could easily use Voyant to do that!