Reading Like a Computer

Assignment 1: Text Analysis with a Computational Tool

Overview

In this assignment, you will select a text corpus and use a computational text analysis tool to explore it, generate insights, and communicate your findings to an audience.

Due Date: Friday, May 1, 2026
Submission Format: Web-based (on your course website)
Length: ~1,500 words + visualizations
Value: 15% of your final grade

Learning Objectives

By completing this assignment, you will:

  1. Apply a computational text analysis tool to a real-world corpus
  2. Generate and interpret visualizations and quantitative results
  3. Ground computational findings in humanistic analysis
  4. Communicate technical work to a non-specialist audience
  5. Critically evaluate the affordances and limitations of computational methods

Assignment Description

Part 1: Selection and Framing (300 words)

Choose a corpus of texts that interests you and articulate a research question or analytical goal. Your corpus could be:

Suggested Corpora:

  • A collection of literary texts (e.g., short stories, poems, novels by a single author)
  • News articles on a particular topic or from a specific publication
  • Social media posts, tweets, or blog posts
  • Historical documents or letters
  • Song lyrics, movie scripts, or other cultural texts
  • Your own dataset (with instructor approval)

Your Framing Should:

  • Describe the corpus (what texts? how many? what time period?)
  • Explain why you chose this corpus
  • Articulate 2-3 research questions you want to explore
  • Indicate what you hope to discover through computational analysis
  • Explain why a computational approach is useful for your questions

Part 2: Computational Analysis (600 words)

Conduct your analysis using one or more of the tools we’ve explored in class:

Tool Options:

  • Voyant Tools — Web-based visualization and analysis
  • AntConc — Corpus analysis software
  • R/RStudio — Statistical text analysis
  • Python — With libraries like NLTK or spaCy
  • Other tools (with instructor approval)

Your Analysis Should:

  • Clearly explain which tool(s) you used and why
  • Describe the analysis process you followed
  • Present 3-5 key findings or patterns you discovered
  • Include 3-5 visualizations (screenshots, graphs, word clouds, etc.)
  • Explain what each visualization shows and what it means

Examples of Analysis Approaches:

  • Frequency analysis: Which words appear most often? How do frequencies change over time?
  • Comparative analysis: How do different texts or corpora differ in their word use?
  • Pattern finding: What clusters or groupings emerge from the data?
  • Sentiment analysis: What emotional tones or attitudes appear in the texts?
  • Stylistic analysis: How do different authors or texts differ stylistically?

Part 3: Critical Interpretation (500 words)

Reflect on what your computational analysis reveals and its limitations.

Address:

  • What do your results suggest about the corpus? What is interesting or surprising?
  • How do these findings relate to your research questions?
  • What humanistic knowledge or context helps interpret these results?
  • What are the limitations of your analysis? (corpus bias, tool limitations, etc.)
  • What biases or assumptions might be embedded in the tool you used?
  • What questions remain unanswered? What would you explore next?

Tone: Thoughtful, critical, and honest about what you don’t know

Format and Submission

Publication

  • Post your analysis on your course website
  • Create a clear title and include the date
  • Make sure all images and links work correctly
  • Include a link from your main course site index

Visual Elements

  • Include 3-5 visualizations from your analysis
  • Caption each visualization clearly
  • Reference visualizations in your written text
  • Consider layout and readability

Structure

  1. Title and introductory paragraph
  2. Section 1: Corpus and Research Questions
  3. Section 2: Analysis and Findings
  4. Section 3: Critical Reflection
  5. Visualization gallery or integrated throughout

Citation

Cite any sources you reference (readings, tutorials, data sources, tools).

Assessment Rubric

See the Rubrics page for detailed evaluation criteria.

Key Areas Evaluated:

  • Method selection and application (15%)
  • Research question and design (15%)
  • Analysis and interpretation (25%)
  • Critical evaluation of limitations (15%)
  • Visual communication (15%)
  • Writing quality and organization (15%)

Resources and Support

Tools and Tutorials

Text Sources

Getting Help

  • Attend office hours to discuss corpus selection and analysis approach
  • Bring questions about tool use to class sessions
  • Review exemplary assignments from previous semesters
  • Participate in peer discussions and feedback sessions

Common Questions

Q: Can I use a corpus I found online?
A: Yes, as long as it’s legally and ethically available. Avoid copyright-protected texts without permission.

Q: What if the tool doesn’t work as I expected?
A: That’s valuable information! Document what happened, explain what you learned, and discuss it in your critical section. Tool challenges are learning opportunities.

Q: Can I use a tool that’s not on the suggested list?
A: Yes, but get instructor approval first. Bring an example or link to class.

Q: How many visualizations do I need?
A: At least 3, no more than 5. Quality over quantity.

Q: Can I work with a partner?
A: No, assignments must be individual work. You may discuss ideas, but writing and analysis must be your own.

Timeline Suggestions

  • Week of April 7: Select corpus; articulate research questions
  • Week of April 14: Begin exploratory analysis; generate initial visualizations
  • Week of April 21-28: Refine analysis; experiment with different approaches; gather final visualizations
  • Week of April 28-May 1: Write critical reflection; revise and polish; post to website