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INFO 3350/6350, Spring 2022, Cornell

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# Information Science 3350/6350

## Text mining for history and literature

### Staff and sections

**Instructor:** [Matthew Wilkens](https://infosci.cornell.edu/content/Wilkens)\
**Graduate TAs:** [Federica Bologna](https://www.linkedin.com/in/federica-bologna-12a570197), [Rosamond Thalken](https://rosamondthalken.com/)\
**Undergrad TAs:** Steven Chen, Naba Deyab, Isabel Frank, Zilu Li, Lindsey Luo, Daniel Riley, Aishwarya Singh, Ray Wang

**Term:** Spring 2022\
**Credits:** 3\
**Mode:** In person except as required by Cornell (```¯\_(ツ)_/¯```)

**Lecture:** MW 9:05-9:55am, Baker Lab 335 [**updated**]\
**Sections:**
  * F  9:05-9:55  Olin 145
  * F 10:10-11:00 Upson 146
  * F 11:20-12:10 Hollister 312
  * F 12:25-1:15  Philips 307
  * *Additional grad section*: F 2:40-3:30pm (Gates 114)

**Office hours:** See [Canvas](https://canvas.cornell.edu/courses/39210)\
**Online sessions and resources:** See the [**Mechanics**](#mechanics) section below.

### Waitlist

Both the course and the waitlist are full. If you are currently on the waitlist, we will send you a Zoom link so that you can attend lectures during the add/drop period.

### Summary

A course on the uses of text mining and other data-intensive techniques to assist analysis of textual humanities materials. Special emphasis on literary and historical documents. Intended for students with programming experience equivalent to [CS 1110](https://classes.cornell.edu/browse/roster/SP22/class/CS/1110) (Intro to Computing Using Python).

### Description

Broadly speaking, the course covers text mining, content analysis, and basic machine learning, emphasizing approaches with demonstrated value in literary studies and other humanistic fields. Students will learn how to clean and process textual corpora, extract information from unstructured texts, identify relevant textual and extra-textual features, assess document similarity, cluster and classify authors and texts using a variety of machine-learning methods, visualize the outputs of statistical models, and incorporate quantitative evidence into literary and humanistic analysis. The course will also introduce some of the more interesting recently published results in computational and quantitative humanities.

Most of the methods treated in the class are relevant in multiple fields. Students from all majors are welcome. Students with backgrounds in the humanities are especially encouraged to join.

### Objectives and learning goals

The primary objective of the course is to build proficiency in text analysis and data mining for the humanities. Students who complete the course will have knowledge of standard approaches to text analysis and will be familiar with the humanistic ends to which those approaches might be put. Secondary objectives include acquiring basic understanding of relevant literary history, of integrating quantitative with qualitative evidence, and of best practices in small-scale project management.

### Mechanics

We will use:

* **GitHub** (right here) to distribute lecture materials, code, and datasets. The current versions of the syllabus (this page) and the [schedule](./schedule.md) are always on GitHub, too. You might want to watch or star this repo to be notified of changes.
* **CMS** to manage problem sets and other code work and to track grades.
* **Canvas** to distribute restricted readings and other non-public materials, including Zoom links.
* **Ed** for Q&A.
* **Google Drive** to build and manage a class corpus.

Links and detailed info about each of these are available via the [course Canvas site](https://canvas.cornell.edu/courses/39210).

Note that you must generally be logged in through your Cornell account to access non-public resources such as Zoom meetings, lecture recordings, Google Drive folders.

### Work and grading

Grades will be based on weekly problem sets (50% in sum), a midterm mini-project (15%), reading responses (10% in sum), a take-home final exam or optional final project (20%), and consistent professionalism (including attendance and participation, 5%). *You must achieve a passing grade in each of these components to pass the course.*

**Graduate students** (enrolled in 6350) must complete a final project in place of the final exam and are strongly encouraged to attend an additional weekly section meeting for 6350 (Fridays, time to be determined).

A small amount of **extra credit** will be awarded for IS/Communications SONA study completion (0.5 course point per SONA credit assigned to this class, up to 1.0 total course point) and for consistent, helpful contributions to Ed discussions (up to 1.0 course point).

### Texts and readings

There is one required textbook for the course:

* Karsdorp, Kestemont, and Riddell. [*Humanities Data Analysis*](https://www.cornellstore.com/adoption-search-results?ccid=335984&itemid=359684).

Readings from this textbook will be assigned for most weeks. All other assigned readings will be available online, either through the open web or via Canvas. See the [schedule](./schedule.md) for details.

There are five textbooks that may be useful for students who wish to consult them. **They are not required and most students will not need them.**

* Guttag. [*Introduction to Computation and Programming Using Python (3rd ed.)*](https://www.amazon.com/dp/0262542366/). Useful for students who need or want a refresher on basic concepts in Python.
* Walsh. [*Introduction to Cultural Analytics and Python*](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html). An intro-level, interactive textbook developed at Cornell that covers material similar to 3350. A good place to start if you're feeling behind on the fundamentals.
* Bengfort, Bilbro, and Ojeda. [*Applied Text Analysis with Python*](https://www.amazon.com/dp/1491963042). A *very* applied book intended for working developers who want to learn the standard Python stack for text analysis.
* Jurafsky and Martin. [*Speech and Language Processing (3rd ed.)*](https://web.stanford.edu/~jurafsky/slp3/). A detailed textbook focusing on many of the core topics in natural language processing. Probably more advanced than most students will require, but a great resource for those who want more technical depth. The linked version is the openly available draft of the third edition. The published [second edition is also available for sale](https://www.amazon.com/dp/0131873210).
* Jockers and Thalken. [*Text Analysis with R for Students of Literature (2nd ed.)*](https://www.amazon.com/dp/B086KZL3DR). An ideal textbook for this class, but in R. Co-authored by one of our PhD TAs!

### Schedule

In general, Monday lectures will introduce new technical material. Wednesday sessions will combine technical instruction with discussion of assigned readings from the scholarly literature. Friday sections are smaller and devoted to focused work on problem sets and to follow-up questions about topics previously introduced.

For the detailed (and updating) list of topics and readings, see the course [schedule](./schedule.md).

### Final exam

A **final exam** in the form of a take-home project is due during the finals period. More information will be posted as that time draws closer.

**Undergraduates** (enrolled in 3350) may elect to complete a project in lieu of the exam. If you take this route, you may work in a group of up to three students. The expected amount of work on the project will be scaled by the number of group members. Except in unusual circumstances, all group members will receive the same grade.

**Graduate students** (enrolled in 6350) **must** complete an independent project in place of the final exam.

### Policies

#### Harassment and respect

All students are entitled to respect from course staff and from their fellow students. All staff are entitled to respect from students and from fellow staff members. Violations of this principle, whether large or small, will not be tolerated.

Respect means that your ideas are taken seriously, that you feel welcome in class settings (including in study groups and online fora), and that you are treated as a full, co-equal member of the class. Harassment describes any action, intentional or otherwise, that abridges the respect owed to every member of the class.

If you experience harassment in any form, or if you would like to discuss your experience in the class, please see me in office hours or contact me by email. The university also has reporting and counseling resources available, including those for [sexual harassment](https://gradschool.cornell.edu/policies/sexual-misconduct-including-harassment/) and for [other bias incidents](https://gradschool.cornell.edu/diversity-inclusion/reporting-bias/).

#### Attendance

This is a class of moderate size that will make frequent use of class time to discuss readings and to debate different approaches to academic inquiry. It also has a waitlist a mile long. For this reason, **attendance (virtual and physical, as circumstances dictate) is required**. When meeting by Zoom, **your camera must be on** or you will be marked absent. If you feel your circumstances require an exception to this policy, you should consult with Professor Wilkens directly to explain why.

Students in mandatory isolation or who are in highly displaced time zones and who have received individual permission are excused from attending Monday and Wednesday lectures. These students should watch the recorded lectures as soon as they are available and post any questions to Ed.

If you need to miss a class meeting, please complete the [absence form](https://forms.gle/ZQqeoqQKsGr9mhkx9) *before the meeting in question* and watch the recorded video of the session you missed once it is available. If you miss section on Friday, a recording may not be available. Consult with your section leader for appropriate steps. In every case, assigned work remains due at the appointed time.

**Note:** *Participation* is much more important than *attendance*. Your grade will not suffer if you make the wise decision to stay home when you might infect others.

#### Slip days

**Late work** is accepted subject to a limit of **ten total slip days** for the semester. You may submit any individual assignment up to **four** days (96 hours) late. The slip day policy *does not* apply to the reading responses, which may not be submitted late, since they are tied to in-class activities.

If you expect to miss a deadline or to be absent for an extended period due to truly exceptional circumstances, contact Professor Wilkens as far in advance as possible so that we can discuss potential accommodations.

#### Regrade requests

If you feel that the graders have made a clear, objective, and significant mistake in assessing your work, you may request a regrade via CMS not later than one week after feedback is released. Remember that this process exists to correct mistakes. This process does not exist to lobby for points.

Regrade requests are typically processed within a week or two of submission. You will be notified of the outcome as soon as it is ready.

We want to give grades that accurately represent our assessment of your understanding of course material. Hence, if you are given a lower score than you should have been, due to an obvious grading error, you should absolutely bring it to our attention. However, we must explicitly mention an additional consequence of the importance of grade accuracy: if we notice that you have been assigned more points than you should have been, we are duty-bound to correct such scores downward to the correct value.

#### Academic integrity

Each student in this course is expected to abide by the Cornell University [Code of Academic Integrity](http://theuniversityfaculty.cornell.edu/academic-integrity/). Any work submitted by a student in this course for academic credit will be the student's own work unless specifically and explicitly permitted otherwise.

Using other people's code is an important part of programming but, for group projects, the code should be substantially the work of the group members (except for standard libraries). Any code used in projects that was not written by the group members should be placed in separate files and clearly labeled with their source URLs. If you have benefitted from online resources such as StackOverflow, list the URLs in comments in your own code, even if you did not directly copy anything.

Project work that relates to your other classes or research is encouraged, but you may not recycle assignments. There must be no doubt that the work you turn in for this class was done for this class. When in doubt, consult with me during office hours.

#### Disabilities

Every student's access is important to us. If you have, or think you may have, a disability, please contact Student Disability Services for a confidential discussion: [sds_cu@cornell.edu](mailto:sds_cu@cornell.edu), 607-254-4545, or [sds.cornell.edu](https://sds.cornell.edu/).

* Please request any accommodation letter early in the semester, or as soon as you become registered with [Student Disability Services](https://sds.cornell.edu/) (SDS), so that we have adequate time to arrange your approved academic accommodations.
* Once SDS approves your accommodation letter, it will be emailed to you and to me. **Please follow up with me to discuss the necessary logistics of your accommodations.**
* If you are approved for exam accommodations, please consult with me at least two weeks before the scheduled exam date to confirm the testing arrangements.
* If you experience any access barriers in this course, such as with printed content, graphics, online materials, or any communication barriers, reach out to me and/or your SDS counselor right away.
* If you need an immediate accommodation, please speak with me after class or send an email message to me and to [SDS](mailto:sds_cu@cornell.edu).

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