Type of Credit: Elective
Credit(s)
Number of Students
Content: The explosion of digital text represents an unprecedented opportunity for social research! This course surveys text pre-processing and analysis on three levels. Topics include:
Students will examine how text analysis is used to conduct social science. They will also apply text analysis to social science questions. Special attention will be given to generative AI and the ways it is rapidly augmenting text analysis.
Audience: Designed as a course for IDAS students, students in IMAS or other graduate programs are also welcome to join. No prerequisites and no prior coding experience are required. Sample code will be provided. And as your instructor, I will walk you through each exercise, step-by-step. No fear! Let’s start coding!
能力項目說明
You will become familiar with:
You will practice:
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
Week |
Date |
Topic |
Assignment |
|
Module 1: Pre-Processing |
|||
|
1 |
2/20 |
Introduction to Text & Text Analysis |
|
|
2 |
2/27 |
Pre-Processing |
|
|
3 |
3/5 |
Exploratory Analysis |
Sun 3/10: Code & Review #1 |
|
4 |
3/12 |
AI Workshop #1 |
Sun 3/17: Team Video #1 |
|
5 |
3/19 |
Application Roundtable #1 |
|
|
Module 2: Analyzing Tokens |
|||
|
6 |
3/26 |
Vectorization |
|
|
7 |
4/2 |
Tagging and Naming |
Sun 4/7: Code & Review #2 |
|
8 |
4/9 |
AI Workshop #2 |
Sun 4/14: Team Video #2 |
|
9 |
4/16 |
Application Roundtable #2 |
|
|
Module 3: Analyzing Documents |
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|
10 |
4/23 |
Machine Learning for Text Classification |
|
|
11 |
4/30 |
Sentiment Analysis |
Sun 5/5: Code & Review #3 |
|
12 |
5/7 |
AI Workshop #3 |
Sun 5/12: Team Video #3 |
|
13 |
5/14 |
Application Roundtable #3 |
Sun 5/19: Poster – Mock-up |
|
Module 4: Analyzing Corpora |
|||
|
14 |
5/21 |
Latent Topics |
|
|
15 |
5/28 |
Semantic Networks |
Sun 6/2: Code & Review #4 |
|
16 |
6/4 |
AI Workshop #4 |
Sun 6/9: Team Video #4 |
|
17 |
6/11 |
Application Roundtable #4 |
Sun 6/16: Poster – Printed |
|
Poster Session – Schedule TBD |
|||
|
18 |
6/18 |
No class. |
|
|
Task |
Points per Assessment |
Percent of Semester Grade |
|
Attendance |
After TWO FREE ABSENCES, each unexcused absence is -5% |
0% |
|
Coding |
4 reviews * 5% each |
20% |
|
Review |
4 tasks * 5% each |
20% |
|
Team Videos |
4 videos * 10% each |
40% |
|
Research Poster |
1 poster & presentation |
20% |
A coding task will be assigned for Modules 1-4. The instructor will provide sample code and data. Students will execute the sample code and make small changes as the task requires.
For each of the four modules, a research review will be assigned. The instructor will specify a computational method or type of data. Students will be asked to find three research papers using that type of method or data, and then to summarize and compare the three papers in a 2-page research review. Students may choose papers in a social science field which interests them.
For each of the four modules, students will work in teams to complete a text analysis project and make a 10-minute video presenting their results. Each project will expand on that module’s coding task and research review. Students will choose a source of text reflecting a social science application of interest to them. Working in teams, students will be asked to conduct an analysis of the text. Teams may use generative AI tools in order to augment their task. Then they will create a team video, also using AI tools if they wish, to present the results of their analysis.
As an end-of-semester project, students will prepare a research poster describing their use of text analysis to answer a social science research question. Posters will have sections on background, the student’s theory and hypothesis, data, analysis, discussion and conclusion. Students will submit a draft of their poster for evaluation, revise the poster based on feedback, print their final poster and present it in an end-of-semester poster session.
Students are encouraged to use generative AI to augment any aspects of all assignments including literature reviews, coding and team videos. If AI-generated results do not fully satisfy assignment criteria, some human intervention may be required in order to complete the assignment and receive full credit.
Moodle Link TBD