Reading Literature With Computers

Science Fiction and Coding

Between the upcoming Pythonic Parashing assignment and the final project, I have quite a bit to talk about this week! Last week I did some brainstorming on what I was considering for the final project, but I have finally figured out for certain what my topic will be: science fiction.

Or, to be more specific, 20th and early 21st century science fiction. I found an online corpus that was publish online in 2019 that created by scouring Good Reads for all reader comments, plot descriptions, and user-generated tags to create a working database of science fiction concepts. This is great, because there’s something like 4,000+ science fiction books to pull form. I plan to look at some of the more common themes, most prevalent authors included in the corpus, as well as the years which featured more science fiction publications and how I can connect that to historical context.

The online library I found is called “100 Years of Science Fiction” and is published using a beautiful computational tool called Openmappr. I would try to embed the visuals, but SVG does not agree with WordPress. Basically it maps all the data in an interconnected network/cloud that is color coded by themes. It’s very interesting to look at, and I’ll try to provide the link for anyone reading this who might be interesting.

As for the Pythonic Paraphrasing project, I’m going to try my best at recreating the Gender Guesser. I’m looking at a combination of Ben Blatt’s work and Hacker Factor’s JavaScript Gender Guesser. While I’m not well-versed in JavaScript, I’m hoping I can translate some of the code into Python to create a working program. I plan to use the corpora the class compiled earlier in the semester to test it. I’ve started on the code a bit, but haven’t made it that far. I’m hoping to sit down with it over the weekend and have a decent rough draft of the code by Monday to work with.

But, that’s all I have for now! I have some work ahead of me and I can only hope it turns out good!

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