Activities
Course activities are broadly defined below. Detailed instructions and grading rubrics will be uploaded to the course website and introduced in class.
Review Videos – 40% of Overall Grade
At the beginning of the semester, students will form teams of 2-3 students based on their shared interests. Several times throughout the semester, the instructor will select a text analysis method and each team will prepare a video reviewing one academic article applying that method. The instructor will provide detailed instructions and a grading rubric in a separate document, but the basic steps are:
- As a team, search for relevant articles. Submit a list of 4-5 articles to the instructor. The instructor will choose one of these for your team to review.
- Individually, read and outline the article. Critically evaluate the article’s research question, arguments, research design, data, methods, analysis and conclusions.
- As a team, compare notes and produce a 10-15 minute Review Video reviewing the article.
- Individually, watch all the Review Videos outside of class.
- As a class, critique and discuss the Review Videos in a Review Roundtable.
Skills Tasks – 40% of Overall Grade
Throughout the semester, with the introduction of each new text analysis method, students will implement that method using sample code and data prepared by the instructor. Skills tasks will be performed and submitted by each student individually. The instructor will provide detailed instructions and a grade rubric in a separate document, but skills tasks will entail:
- Downloading and installing software such as Python, Jupyter Notebook and machine learning libraries.
- Downloading sample code and data.
- Running sample code, line by line, and making minor modifications to the code.
- Saving output and submitting this to the instructor for grading.
Research Poster – 20% of Overall Grade
Toward the end of the semester, each student will select one text analysis method and apply this to a research topic of his or her choice. Each student will summarize their research questions, data, methods, analysis and results in a research poster. Students will present their research posters in an end-of-semester poster symposium. The instructor will provide detailed instructions and a grading rubric in a separate, but the basic milestones include:
- Propose poster – discuss with professor in an in-person consultation
- Collect data – submit data to instructor for approval
- Conduct analysis – submit analysis to instructor for approval
- Design poster – submit digital draft of poster design to instructor for approval and a grade
- Print final draft of poster
- Present poster – present poster in a poster symposium
Lectures and Out-of-Class Preparation
Lectures will be divided into one of three types:
- Lecture & Discussion
- Article Review Roundtables
- Methods Tutorials
- Skills Workshops
- How to review a research article and make a review video
- How to design a research poster
- How to present a poster at a poster symposium
Students are expected to prepare for class in advance and participate in classroom discussions. Preparation for class will entail:
- Complete reading assignments
- Watch Article Review Videos
- Prepare questions for the instructor and peer presenters
Activity-ILO Matrix
Course activities support Intended Learning Outcomes in the following way.
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ILO 1.
Know current methods
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ILO 2.
How to interpret text as data
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ILO 3.
Evaluate methods in research
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ILO 4. Implement methods
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ILO 5.
Communicate methods, results
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In-Class Discussion
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x
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x
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x
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x
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Article Review Videos
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x
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x
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x
|
|
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Article Review Roundtables
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x
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x
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x
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|
x
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Skills Tasks
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x
|
|
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x
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x
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Propose Poster
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x
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x
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x
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x
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Collect Data
for Poster
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x
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x
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Conduct Analysis
for Poster
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x
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|
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x
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Design Poster
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x
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x
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x
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x
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x
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Present Poster
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x
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x
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x
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x
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x
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Schedule & Deadlines
Week of
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Lecture Topics
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Skills Tasks
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Team Reviews
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Poster
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1. Sept 11
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Syllabus
Poster: Proposal Tips
Tutorial: Python 101
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2. Sept 18
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Lecture: Text as Data
Introduce Review Videos
Tutorial: Python Pandas
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Python 101
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Module 1: Text Mining
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Week of
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Lecture Topics
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Skills Tasks
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Team Reviews
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Poster
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3. Sept 25
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Lecture: Text Mining
Poster: Data & Analysis Tips
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Python Pandas
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List: Text Mining
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4. Oct 2
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Lecture: Extracting Data
Tutorial: Extracting Data
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Proposal
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5. Oct 9
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Lecture: Web Scraping
Tutorial: Web Scraping
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Vid: Text Mining
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6. Oct 16
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Review: Text Mining
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Data
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Module 2: Semantic Units
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Week of
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Lecture Topics
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Skills Tasks
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Team Reviews
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Poster
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7. Oct 23
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Lecture: Semantic Units
Poster: Design Tips
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Text Mining
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List: Semantic Units
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8. Oct 30
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Lecture: Tokens
Tutorial: Tokens
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Analysis
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9. Nov 6
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Lecture: Latent Topics
Tutorial: Latent Topics
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Vid: Semantic Units
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10. Nov 13
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Review: Semantic Units
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Module 3: Sentiment & Meaning
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Week of
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Lecture Topics
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Skills Tasks
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Team Reviews
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Poster
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11. Nov 20
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Lecture: Sentiment, Meaning
Poster: Presentation Tips
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Semantic Units
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List: Sentiment, Meaning
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12. Nov 27
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Lecture: Sentiment Analysis
Tutorial: Sentiment Analysis
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Design
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13. Dec 4
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Lecture: Word Embeddings
Tutorial: Word Embeddings
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Vid: Sentiment, Meaning
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14. Dec 11
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Review: Sentiment, Meaning
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Module 4: Structural Patterns
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Week of
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Lecture Topics
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Skills Tasks
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Team Reviews
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Poster
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15. Dec 18
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Lecture: Structural Patterns
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Sentiment, Meaning
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List: Structural Patterns
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Presentation
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16. Dec 25
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Lecture: Semantic Networks
Tutorial: Semantic Networks
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17. Jan 1
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Lecture: Sequence Analysis
Tutorial: Sequence Analysis
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Vid: Structural Patterns
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18. Jan 8
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Review: Structural Patterns
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Structural Patterns
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Grading Rubric
Activity
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Percent of Overall Grade
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Non-Graded Requirements
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In-Class Participation
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-
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After TWO FREE ABSENCES,
each unexcused absence is -5%
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Article Review Videos
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40%
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Skills Tasks
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40%
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Poster Proposal & Consult
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-
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Required to receive Poster grade
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Poster Data & Consult
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-
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Required to receive Poster grade
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Poster Analysis & Consult
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-
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Required to receive Poster grade
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Poster Draft & Consult
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-
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Required to receive Poster grade
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Poster Presentation
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20%
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Attendance Policy
Class attendance is essential to students’ achieving the Intended Learning Outcomes; however, students must also balance learning with other life priorities. As a result, this course allows students to have TWO FREE ABSENCES during the semester. After two free absences, each unexcused absence will subtract 5% from the student’s overall grade. Arriving to class late, or leaving class early, by more than 10 minutes will be counted as half an absence.
Excused absences will not affect students’ grades and do not count as the student’s two free absences. Excused absences require:
- a note from a doctor, in the case of a medical emergency
- a note from an administrator, if absent due to an official university event
- the instructor’s discretion, in the case of a family emergency (e.g., funeral)
Absences due to the following reasons will not be excused:
- trouble with a vehicle, parking and traffic
- vacations
- jobs and internships
- absence due to working on homework or a class project
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Week 1 – September 11 – Introductions
- Syllabus*
- Intro to Text as Data
Week 2 – September 18 – Text as Data
- Anne Mische’s article
- Intro to Matt Salganik book
Week 3 – September 25 – Text Mining: Extracting Data
Week 4 – October 2 – Text Mining: Web Scraping
Week 5 – October 9 – Video Review Roundtable on Text Mining
- Article Review Videos on Text Mining
Week 6 – October 16 – Semantic Units: Tokens
Week 7 – October 23 – Semantic Units: Latent Topics
Week 8 – October 30 – Video Review Roundtable on Semantic Units
- Article Review Videos on Semantic Units
Week 9 – November 6 – Midterm Exam: Individual Consultations
Week 10 – November 13 – Sentiment & Meaning: Sentiment Analysis
11. Week of November 20 – Sentiment & Meaning: Word Embeddings
12. Week of November 27 – Video Review Roundtable on Sentiment & Meaning
- Article Review Videos on Sentiment & Meaning
13. Week of December 4 – Structural Patterns: Semantic Networks
14. Week of December 11 – Structural Patterns: Sequence Analysis
15. Week of December 18 – Video Review Roundtable on Structural Patterns
- Article Review Videos on Structural Patterns
16. Week of December 25 – TBD
17. Week of January 1 – TBD
18. Week of January 8 – TBD
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