- Mandell, Laura. “Gender and Cultural Analytics: Finding or Making Stereotypes?” Debates in the Digital Humanities. Eds. Matthew K. Gold and Lauren Klein. Minnesota: U of Minn Press, 2019. [NOTE: Prof. Mandell will be at the Graduate Center to give a talk about this piece on Friday, September 20th at 4:00 PM in room 4406. If it is at all possible to attend, I encourage you to do so.]
- Rawson, Katie and Trevor Muñoz. “Against Cleaning.” Debates in the Digital Humanities. Eds. Matthew K. Gold and Lauren Klein. Minnesota: U of Minn Press, 2019. [NOTE: Dr. Rawson will be at the Graduate Center to give a talk about this piece on Friday, September 20th at 4:00 PM in room 4406. If it is at all possible to attend, I encourage you to do so.]
- Catherine D’Ignazio and Lauren Klein. “ChapterTwo: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints.” Data Feminism. Preview available online from MIT Press.
- Ted Underwood. “Where to Start with Text Mining.” The Stone and the Shell, 14 Aug. 2012, https://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/.
- UPDATE: You can download a zip file of the Week 5 assignment on our GitHub repository. Please work through this notebook, answer the questions, and turn it in (via email as a PDF) by the time we return to class on 10/15. Note that you do NOT need to continue on to the sentiment analysis section. That is an optional challenge for those who are interested.
More on tokenizing for sentiment analysis: http://sentiment.christopherpotts.net/tokenizing.html
As we go on, it may become challenging to keep up with the notebooks without some more basic fluency in Python, as that’s the environment where we’re doing most of our work. There are a few fundamental concepts that will be helpful for you to be familiar with as we move ahead. I want to focus on this word familiar because I’m using it purposefully. In fact, you might even want to check out the definition I’m working from in the Cambridge Dictionary: https://dictionary.cambridge.org/us/dictionary/english/familiar.
familiar: adj. B1 easy to recognize because of being seen, met, heard, etc. before:
- There were one or two familiar faces (= people I knew).
- The house looked strangely familiar, though she knew she’d never been there before.
- The street was familiar to me.
In other words, I’m encouraging you to begin to pick up aspects of Python that help familiarize you with a procedural way of thinking such that you can recognize (if not cut and copy and use) segments of Python that are useful to you. These resources will help introduce you to Python, but it’s only through reading, tinkering with, and experimenting with it that you’ll develop that familiar feeling. All of which is to say… it’s ok if you have to keep looking things up. You’re not cheating if you have to google a line in the notebooks and search the web for what it does. I don’t expect you to be following along yet, but let’s revisit this concept of “familiar” in a few weeks and see if it doesn’t seem more reasonable then.
Meanwhile, here are some terms to look up and some resources to look at to help you familiarize yourself with Python as begin to take a deeper dive together:
- Data types: numbers (integers, floats, etc), string (characters), list, dictionary, tuple. You can find a good description of them here: https://www.programiz.com/python-programming/variables-datatypes.
- Modules and packages: Computers are very good at reproducing tasks over and over again. If the same process can be used in several different places, then why not save yourself (and others) work by recycling it? Modules and packages do that, and this short tutorial goes into some detail about how and why: https://www.learnpython.org/en/Modules_and_Packages.