Introduction to Computational Literary Analysis, Summer 2022

Table of Contents

Welcome! Here you’ll find all the course information for Introduction to Computational Literary Analysis, a course taught at UC-Berkeley, every summer since 2018.

Course Details

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This course is an introduction to computational literary analysis, which presumes no background in programming or computer science. We will cover many of the topics of an introductory course in natural language processing or computational linguistics, but center our inquiries around literary critical questions. We will attempt to answer questions such as:

The course will teach techniques of text analysis using the Python programming language. Special topics to be covered include authorship detection (stylometry), topic modeling, and word embeddings. Literary works to be read and analyzed will be Wilkie Collins’s The Moonstone, Katherine Mansfield’s The Garden Party and Other Stories, and James Joyce’s Dubliners.


Although this course is focused on the analysis of literature, and British literature in particular, the skills you will learn may be used to computationally analyze any text. These are skills transferable to other areas of the digital humanities, as well as computational linguistics, computational social science, and the computer science field of natural language processing. There are also potential applications across the humanistic disciplines—history, philosophy, art history, and cinema studies, to name a few. Furthermore, text- and data-analysis skills are widely desired in today’s world. Companies like Google and Facebook, for instance, need ways to teach computers to understand their users’ search queries, documents, and sometimes books. The techniques taught in this course help computers and humans to understand language, culture, and human interactions. This deepens our understanding of literature, of our fellow humans, and the world around us.


This course presumes no prior knowledge of programming, computer science, or quantitative disciplines. Those with programming experience, however, won’t find this boring: the level of specialization is such that only the first few weeks cover the basics.

Course Structure

Although this is usually a classroom-taught course, due to the global pandemic, this course is taught online-only, for the moment. This will require a lot of adaptation from everyone, and it won’t be easy. That said, I’ll be trying my best to make this course flexible, and doable from different timezones.

Lecture Videos

In place of in-person lectures, I’ll post lecture videos. Each video is between 30-70 minutes long, and is required viewing. Please watch the lecture videos before coming to discussion sections, so that we can all discuss it synchronously. Links will be posted to this syllabus. Please resist the urge to watch lecture videos too far in advance, since they may change as I revise the course content.

Discussion Sections

In place of in-person classroom dialogue and activities, we’ll hold discussion sections online, using Zulip, at Zulip is a text-based chat platform, with email-like threading. You can use it to join an existing discussion thread, or create a new one.

Attendance in these discussions is required. If you need to participate asynchronously, for whatever reason, just let me know in advance (on Zulip). Participating asynchronously means that you still engage in our conversations, just at a later point in the day.

As in a traditional classroom, some days you will want to speak (i.e., write in the chatroom) more than others, and that’s fine. But please say something thoughtful at least once per class. This way there is a record of your participation.

Course Communication

Feel free to chime in on the course chat throughout the week, with any questions or comments you might have. I’ll usually be there once every couple of days. Please use the public channels for any course-related questions you have, unless they are of a private nature (e.g., grades), in which case please message me privately on Zulip, as I will answer faster there than through email. Discussion about specific textual passages might be better placed in annotations, in the margins of the text, using our annotation platform. See Annotations, below.


These are synchronous videoconferences that happen every week, on Friday, from 12:00–13:00, Berkeley time, here on Jitsi. They are less formal than the discussion sections, and an ideal place to come and chat about the readings and/or programming assignments in real time. I recommended you attempt the homework assignments before coming, so that you can ask any questions you have about them during the lab. You’re also welcome to join and just quietly work for the hour. I won’t take attendance, but these labs are strongly recommended.

Getting Started

To get set up for this course, you will need:

Now that we have that, let’s get started! First, let’s set up a couple of accounts:

  1. Create a GitHub account. Unless you’re already well-established there, please use your real name (or English name / preferred name, etc) as your username, and add a picture of yourself.
  2. Use that account to log into our Zulip chatroom. (Click “sign up,” then “sign up with GitHub.”)
  3. Introduce yourself to everyone in the chatroom.
  4. Sign up for a user account on, our annotation platform.
  5. Download and install Anaconda, a Python distribution, which contains a lot of useful data science packages.

Extra Resources

You will likely need some extra help at some point, either for the literary aspect of the course, or the technological aspect. Don’t worry. That’s totally normal. Here are a few resources:

Programming Resources

If you want some extra help, or want to read a little more about some of the things we’re doing, there are plenty of resources out there. If you want a second opinion about a question, or have questions that we can’t answer in the chatroom, a good website for getting help with programming is StackOverflow. Also, the Internet is full of Python learning resources. One of my favorites is CodeCademy, which has a game-like interactive interface, badges, and more. There’s also the fantastic interactive textbook How to Think Like a Computer Scientist, which is the textbook for Computing in Context, the introduction to Python at Columbia’s Computer Science department.

Resources related to text analysis include, but are by no means limited to:

A colleague and I have also put together a few guides for beginning programming:

Literary Resources

If you’re feeling like you need some help catching up with literary-critical terminology, or traditions of scholarship, here is a list of useful reference volumes, some of which are available online:


Coursework falls into three categories:

And of course, there are three course readings: one novel and two short story collections. Reading these closely is crucial: this will allow you to contextualize your quantitative analyses, and will prepare you for the close reading tasks of the final paper.


All readings are provided in digital form on the course website. They are one novel and several short stories:

If you prefer to read on paper, or to supplement your reading with background information and critical articles, I highly recommend the Broadview and Norton Critical Editions. They are full of interesting essays and explanatory notes.


For each reading assignment, please write 2-3 annotations to our editions of the text, using Links are provided below. You’ll have to sign up for a account first. As above, please use your real name as your username, so I know who you are. You may write about anything you want, but it will help your final project to think about ways in which computational analysis might help you to better understand what you observe in the text. Good annotations are:

You may respond to another student’s annotation for one or two of your annotations, if you want. Just make your responses equally as thoughtful.


Four short homework assignments, of around 10 questions each, will be assigned, and are due the following week, on Monday, before our discussion starts. Jupyter notebook templates for each will be provided. Since we’ll review the homework answers at the beginning of each week, late work cannot be accepted. Please submit homework assignments to me via email—

Feel free to consult with others, on Zulip, for hints or directions for homework problems. Just don’t share any answers verbatim, and make sure that your work is ultimately your own.

Final Project

The final project should be a literary argument, presented in the form of a short academic paper, created from the application of one or more of the text analysis techniques we have learned toward the analysis of a text or corpus of your choosing. Should you choose to work with a text or corpus other than the ones we’ve discussed in class, please clear it with me beforehand. Your paper should be a single Jupyter notebook, including prose in Markdown, code in Python, in-text citations, and a bibliography. A template will be provided. The length, not including the code, should be about 2,000 to 3,000 words (I provide a script you can use to count your words). You’re allowed a maximum of three figures, so produce plots selectively.

During the final week of class, we’ll have final project presentations. Your paper isn’t required to be complete by then, but you’ll be expected to speak about your project for 4 minutes. Consider it a conference presentation.

Final papers will be evaluated according to the:

As with homework, please submit these on CourseWorks, or email them to me if you don’t have access to CourseWorks. You may optionally submit your final project to the course git repository, making it public, for a 5% bonus.


Note: this schedule is subject to some change, so please check the course website for the most up-to-date version.

Week 1: Introduction to Python for Text Analysis

Unit 1.1. Tuesday, 2022-07-05: Course intro.

Unit 1.2. Wednesday, 2022-07-06: Installing Python. Python 2 v. 3. Jupyter. Strings.

Unit 1.3. Thursday, 2022-07-07: Working with strings, lists, and dictionaries.

Week 2: Basic Text Analysis

Unit 2.1. Monday, 2022-07-11

Unit 2.2. Tuesday, 2022-07-12

Unit 2.3. Wednesday, 2022-07-13

Unit 2.4. Thursday, 2022-07-14

Week 3: Word Frequency Analyses

Unit 3.1 2022-07-18: Pandas and distinctive words.

Unit 3.2 2022-07-19: N-grams and narrative-time analysis.

Unit 3.3 2022-07-20: WordNet and WordNet-based text analysis. Part-of-speech analyses.

Unit 3.4 2022-07-21: Downloading, using, and iterating over corpora.

Week 4: Linguistic Techniques I

Text: Katherine Mansfield, The Garden Party and Other Stories Tools: NLTK, SpaCy

Unit 4.1 2022-07-25: Review of Week 3 and Homework 3. Corpus vectorization with Scikit-Learn. TF-IDF. Stylometry.

Unit 4.2 2022-07-26: Comparative stylometry. Corpus-DB.

Unit 4.3 2022-07-27: Stylometry, continued.

Unit 4.4 2022-07-28: Topic modeling with LDA. Quote parsing.

Week 5: Linguistic Techniques II

Text: James Joyce, Dubliners Tools: SpaCy

Unit 5.1 2022-08-01: Review of Week 4 and Homework 4. Using SpaCy. Named entity recognition.

Unit 5.2 2022-08-02: Intro to final project. Sentiment analysis. Macro-etymological analysis.

Unit 5.3 2022-08-03: Sentence structure analysis using SpaCy.

Unit 5.4 2022-08-04: Social Network Analysis

Week 6: Advanced Topics

Tools: Scikit-Learn, SpaCy

Unit 6.1 2022-08-08: About the Final Project

Unit 6.2 2022-08-09: Extras

Unit 6.3 2022-08-10: Extras

Unit 6.4 2022-08-11: Final project presentations. Wrap-up.

2022-08-12: Final open lab.

2022-08-13: Final projects due.