Spring 2027

Introduction to Text Mining

Introduction to Text Mining covers computational methods for processing and analyzing text data, including NLP pipelines, text classification, information extraction, and ethical considerations.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Aaron Enright

The course required four major homework assignments, though one student noted the grading feedback for these tasks was unclear.

What you’ll be able to do

  • Understand and apply the general natural language processing (NLP) pipeline to process text data

  • Understand and use computational models to represent text contents and meanings

  • Perform exploratory text analysis to understand the contents, topics, and characteristics of a text corpus

  • Design, implement, and evaluate solutions to categorize texts and extract information from texts

  • Understand ethical issues and concerns related to natural language processing and language technologies

  • Critically evaluate and compare different text mining solutions

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Junior standing and satisfied Quantitative Reasoning (QR) A, or graduate/professional standing

      • Junior standing
      • satisfied Quantitative Reasoning (QR) A
      take all
    • graduate/professional standing
    take one
LIS 501

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of
    • All of
      • Junior standing
      • satisfied Quantitative Reasoning (QR) A
    • graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

AARON ENRIGHT is recorded teaching in Fall 2023, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 500

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Aaron Enright

Students described the class as easy with no exams, but opinions on Aaron Enright’s lecturing style varied significantly between reviews.

Recent recorded grades — Spring 2025: 2.89 GPA, 51.9% A/AB (n=27 letter grades); Fall 2025: 3.77 GPA, 91.4% A/AB (n=35 letter grades); Spring 2026: 3.74 GPA, 86.1% A/AB (n=36 letter grades).

difficulty & workload

Past student reviews · Aaron Enright

The course required four major homework assignments, though one student noted the grading feedback for these tasks was unclear.

Past student reviews · Aaron Enright

One student found Enright’s lectures boring and his in-class quizzes annoying, while another appreciated his humor and organized Canvas setup.

Topics

  • Text data preparation, preprocessing, and models of text content
  • Exploratory text analytics, classification, and information extraction
  • Ethical issues in natural language processing

Skills

  • Apply the NLP pipeline to process text data
  • Design and implement text classification and information extraction solutions
  • Perform exploratory text analysis on corpora
  • Evaluate ethical issues in NLP and language technologies

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
LIS 501 · Spring 2027

Introduction to Text Mining

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:544:026056",
    "course_id": "LIS 501",
    "course_uid": "course_f34e4c11ca636626801b25ae",
    "term_id": "1274",
    "source_course_id": "026056",
    "source_subject_id": "544",
    "title": "Introduction to Text Mining",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "a8e5fd7397a81eacd2892539f1bdcf88dd614db65504e32d31d717c164ef8de1",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}