This is a course is an introduction to methods of text analysis for digital humanists and humanistic social scientists. Students will be able to apply a feminist lens as they develop the following skills:
- Craft and support definitions of key terms: feminism, text, analysis, and method(s).
- Describe examples of how text analysis is used as part of humanities research.
- Identify methods and approaches humanists use to analyze texts computationally.
- Develop functional literacy in Unix, Git/GitHub, Python, natural language processing, and select machine learning methods as part of a computational text analysis workflow.
- Evaluate the effectiveness of text analysis methods for addressing humanities questions.
- Describe challenges humanities scholars confront working with text as data, conceptualization, operationalization, and analysis.
- Use data and computationally-based reasoning to substantiate their position in conversation, prose, and code.
Note: This course engages in methods drawn from or shared with data science, Python, and/or computational linguistics; however, it is not a course in which becoming a data scientist, Python programmer, or computational linguist is the objective.
- Class Website: The course website will have all of the information on this syllabus and more. Immediately following the first class, the website will be the authoritative source of information about the course. Please check it regularly for updates to the schedule, resources, blog posts, and the syllabus itself: https://femethods2020.commons.gc.cuny.edu/.
- Readings – will be available either through the Mina Rees Library’s electronic databases, as PDFs in our CUNY Academic Commons group, or as links to sources available on the open web. Updates will happen weekly, so check the schedule
- CUNY Academic Commons account – If you do not already have one, please register here: https://commons.gc.cuny.edu/register/.
- Class Commons Group: Once you have your Commons account, request access to our class group here: http://cuny.is/group-methods-of-text-analysis-fall-2020.
- Computer – The work for this course requires that you have sustained access to a computer where you can download and install software. You should have a computer that you can use that can run Anaconda during class. If you need one for the class period, please contact me, and we can arrange for you to borrow a MacBook Pro. Discounts for purchases of computers are available to GC / CUNY students.
- Anaconda – Please download and install the open-source Anaconda Distribution. You will need the Python 3.7 64-bit version. When you download and install Anaconda, these will be included automatically. (Please do not use miniconda.)
- Jupyter Notebooks – This should be included in your Anaconda Distribution install.
- Python 3.7 and assorted packages – Weekly assignments will require you to download and install the packages that we will use. These are subject to change, but please be sure to check each assignment to make sure that you have downloaded everything you need to start your work.
- If you already have Anaconda installed, please just make sure that your copy is up to date. If you already have Python installed (or another version of Python), your questions may have answers here: https://docs.anaconda.com/anaconda/user-guide/faq/
- We will do installations of additional packages as we go. The most important is NLTK.
- GitHub account – You will need an account on GitHub. If you do not have one already, please create one here: https://github.com/. Please send it in an email by the 2nd class. Activities will be made available through a GitHub repository. Some assignments may be turned in using this platform. TBD.
- Microsoft Teams – information forthcoming.
- DataCamp account – Following our first class meeting, you will be invited to join a DataCamp classroom. This account will give you access to content that is generally available through a paid subscription to the site. Access will last through December.
Assignments & Assessment
Assessment for the course is designed to support the course’s objectives: to develop a functional literacy in text analysis that allows you to identify, describe, evaluate, and interpret humanities questions about text explored through computational methods. We will focus on assignments and activities that move you closer toward becoming a participant within a digital humanities community. Consequently, assessment will emphasize students’ ability to identify salient questions, ethical concerns, practical challenges, and disciplinary resistances related to computational text analysis in the humanities through writing, conversation, and code.
Only a couple of assignments are required: three early Jupyter notebooks and the final portfolio (20%). Otherwise, students have flexibility to determine the assignments that best fit their educational goals for the course up to a maximum number of assignments. Complete assignments submitted on time receive full credit.
Jupyter Notebook and/or DataCamp Assignments (5% each, max 70%)
Each week, there will be hands-on, applied assignments for students to complete which will take the form of either a Jupyter Notebook (an open-source browser application for creating and sharing documents that run live code, visualizations, and text) or a tutorial from DataCamp.
Jupyter notebook assignments will either ask you to complete a tutorial in which you are guided through the steps required to complete the text analysis task or where the notebook comes already coded such that you simply need to run each cell (and possibly do some minor bug checking). The emphasis on these notebooks will be on your annotations. You will be asked to use markdown (a lightweight encoding for text formatting) to make observations and evidence-based arguments that connect weekly reading assignments to the week’s code.
Notebook assignments will be considered “turned in” when you have saved a copy of the notebook file in our shared Dropbox folder.
All Jupyter assignments with annotations and cells run will receive full credit regardless of accuracy. All completed DataCamp tutorials will receive full credit (maximum 4).
Jupyter notebook assignments: Maximum 14
- REQUIRED notebooks: Week 2, 3, and 4
Datacamp Tutorials: Maximum 5
Class participation is a significant part of this course, but the shape that “participation” may take might look different from a situation where we were meeting in the same physical space. This semester will continue to include weekly synchronous meetings. Participation, therefore, might include any variety of engagements that you have with the group, including but not limited to: responses to discussion questions in a discussion forum, contributions during breakout group discussion and participation in group presentations, volunteering to share your Jupyter notebook and walk through activities, offering to assist classmates who are having trouble with their assignments, or bringing new questions for us to consider during our live conversations.
Given the class’s feminist orientation, it should go without saying that constructive contribution to the class. We will begin with the assumption that each member of the class brings vital knowledge, experience, and perspectives that will help us in our pursuit of an answer to the semester’s guiding question: Can there be a feminist text analysis? Constructive contributions to class are characterized by an individual and collective reflectiveness about traditional assumptions of power and privilege and one’s willingness to actively work against reproducing inequity. In other words, how might your participation (in the way that we communicate and compute) resist reproducing systems of power and privilege surrounding technology and computation? Consciousness-raising is an important part of any feminist practice, but it is not always easy–either to raise issues or to admit fault. Constructive class participation fosters an atmosphere of trust that can be transformative. (For those looking for additional information on how to be an ally, you may consider reading this brief post: “How to be an Effective Ally.”)
Blog posts (5% each – max 20%)
Blog posts represent original writing that engages through critical discourse with a tutorial, guide, current article, or book that describes or uses text analysis methods in a humanities discipline. Each week, we will read chapters, portions of articles or groups of articles. Blog posts demonstrate that a student has read beyond the original assignment. Blog posts must address a public audience, which includes other students in the class and the digital humanities community. They may take the shape of a review, a reflection, or show how you made use of the reading in a small project of your own. Only one blog post may be submitted per week (defined by the start of class each week).
Roundtable / Oral Presentations (10%)
In the final class meeting, we will hold an academic roundtable. The roundtable is a common format for presenting academic arguments or prompts that are designed to engage the audience in conversation. Students will collectively propose 2 panels, and write up to 200-word abstracts for their round table presentations, which will be posted one week before the in-class event. On the final day of class, students will have the opportunity to participate as moderators and presenters. All students are expected to attend this class regardless of whether or not they will be presenting. Students who choose not to present will be expected to engage with presenters through questions, responses, and feedback. We will invite outside guests to visit and participate in our roundtable.
Final Portfolio (20%)
All students are required to submit a final portfolio. The portfolio should include a copy of all of your weekly assignments, and a 5 page, typed, position paper written for an academic audience with appropriate MLA or Chicago Manual Style and including appropriate citations. The final position paper will make a case for your position on the question: “Can there be a feminist text analysis?” It will draw from examples in the notebook assignments as well as secondary sources. Final portfolios can take a variety of formats, ranging from a combined PDF document to a web portfolio to a GitHub repository. We will discuss in more detail during the semester.
- Please check the website regularly, as this is where the most current version of the syllabus and assignments can be found.
- Our class Commons group (http://cuny.is/group-methods-of-text-analysis-fall-2020) will be the primary vehicle for the distribution of information and for file sharing. Please make sure that your options for the group are set to receive individual emails for each post to the group forums. Students are encouraged to share information, converse, ask questions that are on topic and of direct relevance to the course via the group forum. Conversation online should reflect the same values we expect to see from one another in face to face conversation. [Microsoft Teams may increasingly take over this function throughout the semester. Before that happens, an announcement will be made.]
- Files will be shared through the forum and also in the Files section of the Commons group.
- To contact the instructor directly, please use email. Twitter and other social media methods may be tempting, but they will not receive a response for class purposes.
- Email will be replied to within 24-48 hours. If you have not received a response to your email after 48 hours, please follow up.
- If you anticipate missing a class for any reason, please send an email in advance to let me know. Open lines of communication in advance of problems are always preferable to finding out after the fact.
A note on pedagogy and productive vs. unproductive discomfort
I believe that feminist practice begins in the classroom, and experimentation and non-traditional approaches to learning have always been a part of feminist and critical pedagogy. At the same time, taking into consideration Maslow’s hierarchy of needs, everyone needs some sense of stability, safety, and acceptance in order to take the risks required for learning. Whether or not you subscribe to the idea of learning modalities (which is to say that people are inclined toward particular sensory channels or pathways to learn) or growth mindsets (skills grow through effort, implementation of new strategies, and responsiveness to feedback), this course–at its best–will push each of us toward productive discomfort. Discomfort or frustration, however, can often be internalized as fault or failure. Chances are that if you made it to graduate school, it’s because you learned how to learn in the ways most often found in a classroom, so feeling this frustration is likely to bring with it anxieties about “failure.” This course will ask you to be reflective about and responsive to that feeling. If you’re not uncomfortable or frustrated at all–then something’s gone awry. Some stress is necessary, and even healthy, for the learning process. HOWEVER, there are limits to the value of frustration. The goal of this course is to emphasize the productive part of productive discomfort. If you ever feel that the stress, frustration, or discomfort you are feeling as part of the learning process has tipped into the unproductive territory, where you are no longer feeling productive and able to learn, please reach out for help. You can set up an appointment with me or come to office hours. You can reach out to other students. You can meet with the MA / MS advising fellows for help with assignments, and you can go to the GC Digital Fellows’ Office Hours (TBD). I cannot emphasize this enough: please communicate what you need. Reach out. Ask for help.