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Data Science Final Year Topic: Evaluation of Text Survey Theme Coding Agreement

This Data Science final year project investigates comparing automated theme assignment with human coding of short responses, with explicit data definitions and reproducible analysis rather than invented findings.

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Why choose this project topic?

This topic turns comparing automated theme assignment with human coding of short responses into a measurable analytical question. Comparing macro F1, coder agreement and ambiguous-response rate gives a student material for visual results, methodological criticism and a discussion of how the data's limitations change the conclusion.

How does text survey theme coding agreement affect comparing automated theme assignment with human coding of short responses, as measured by macro F1, coder agreement and ambiguous-response rate?

Choose an accessible licensed or synthetic dataset, agree the unit of analysis and narrow comparing automated theme assignment with human coding of short responses to a reproducible comparison your supervisor can review.

Proposed project objectives

  1. 01Define the data, assumptions and comparison design for comparing automated theme assignment with human coding of short responses.
  2. 02Use consented anonymous or synthetic feedback, develop a coding guide and compare classifier output with independently reviewed labels.
  3. 03Compare macro F1, coder agreement and ambiguous-response rate and report uncertainty and sensitivity to analytical choices.

A suggested research approach

Use consented anonymous or synthetic feedback, develop a coding guide and compare classifier output with independently reviewed labels. Confirm the data licence and variable definitions before analysis. Preserve an untouched evaluation set where relevant, document exclusions and missingness, and report macro F1, coder agreement and ambiguous-response rate with uncertainty or sensitivity checks rather than selecting only favourable results.

What you will need

  • Anonymous or synthetic text responses
  • Theme coding guide
  • Independent label review

Keep your project scope clear

Themes depend on coding choices and must not be used to infer individual personality or sensitive traits.

Data Science project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

  1. Chapter 1Introduction
  2. Chapter 2Literature Review
  3. Chapter 3Research Methodology
  4. Chapter 4Presentation and Analysis of Results
  5. Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

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