An introduction to text analysis for literature with foundational overview of considerations for approaching computational text analysis in the humanities. This workshop will cover a) gathering text corpus, b) copyright considerations c) data cleaning, d) an introduction to the computational software tools e) reading the output and analysis that may include word frequencies, cluster analysis, wide spectrum analysis and topic modeling, and f) a general overview of common questions asked in computational literary studies. This workshop is an introduction to working with text as data in the humanities.
| Software: |
Voyant, (may include Intelligent Archive) |
| Duration: |
120 min |
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Room description:
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Some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering
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| Prerequisites: |
None |
| Skills Taught / Learning Outcomes: |
- Introduce key terms and definitions
- Discover where to gather text corpora
- Consider issues of digitizing and cleaning text
- Explore Various Text Analysis Methods
- Inquire into Commonly Asked Research Questions
- Test Software Application
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| Class Materials: |
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| Related Classes: |
Introduction to R
Introduction to Python
Introduction to ATLAS.ti
Digital Tools for Qualitative Data Analysis
Extracting Text Using Optical Character Recognition (OCR)
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| Additional Training Materials: |
Text as data guide
Text analysis tools by David L. Hoover
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| Feedback: |
bit.ly/feedbackds |