This course (Methods of Text Analysis: Feminist Text Analysis – DHUM 72500 & DATA 78000) will follow guiding questions such as: “Can there be such a thing as feminist text analysis?” and “What does it mean to do computational text analysis in a humanities context?” Through reading and practice we will examine the problematic racist, sexist, colonialist, capitalist, and gender-normative assumptions activated through algorithmic methods and the role humanistic inquiry might play in computational text analysis–particularly feminist inquiry and critique. Finally, we consider how humanities and humanistic social scientists might formulate effective research questions for and methods of text analysis.
We will take a very different approach to the topic “methods of text analysis” than typical introductory courses typically do. While we will spend some time on the mechanics, the dominant use of our time will be directed toward developing what we will call a “functional literacy.” The course begins with a 4 week introductory period in which we establish a shared critical vocabulary around terms such as “feminist” (as well as gender/sex), “text” (considering the term from several disciplines) and “analysis” (eg. close reading / encoding / description). We will ask what it means to analyze language with computers from a feminist and humanist perspective by developing our skills as “resistant readers.”
Our emphasis in this class will be on shaping effective research questions over programming mastery. We will ask questions: How does the language of analysis draw on Western traditions of empiricism in which “the text” occupies a position of authority over other forms of representation? What is the difference between “text analysis” and “philology”? What is being “analyzed” when we count, tokenize, measure, and classify texts with computers? And, importantly, how do the questions we are asking align with the methods we are using?
The course is organized around the five stages of the text analysis process as articulated in our first reading: “How we do things with words: Analyzing text as social and cultural data,” which can be downloaded here: https://arxiv.org/pdf/1907.01468.pdf. While students will receive materials to help them learn Python and to develop their own text analysis projects, creating an independent or group text analysis project will not be the objective of the course or the source of evaluation. Nevertheless, students will be asked to develop a literacy in Python and packages frequently used to perform text analysis.
Speaking of evaluation, you may find this course is very different from others you have encountered thus far. Please read the syllabus carefully so that you understand the opportunities to earn credit. Imagine the course as if it were a “Choose Your Own Adventure” book. There will be far more assignments available for you to complete than you will need. There are very few required assignments: the first 3 Jupyter notebook assignments and a Final Portfolio, which includes a five-page position paper as the introduction to all of your completed assignments from the semester. Weekly assignments will include readings and hands-on activities, such as Jupyter notebook assignments and DataCamp course modules; up to 2 blog posts; a project presentation, a reading report, an abstract for a conference presentation, and presenting as part of a public roundtable.
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