Grounded Theory
Specialist agents develop codes, categories and analytic memos. Auditing and targeted revision check the grounding of emerging interpretations.
Sibonelelo’s contributionL4QA connects Grounded Theory, sentiment analysis and interactive visualisations so researchers can follow an insight back to the words that support it.
Interviews, transcripts & qualitative text
Each contribution tackles a different part of the qualitative research process. Together, they connect interpretation with the material behind it.
Specialist agents develop codes, categories and analytic memos. Auditing and targeted revision check the grounding of emerging interpretations.
Sibonelelo’s contributionA comparison of unified and modular workflows for sentiment at document, aspect-category and aspect-term levels.
Sithokomele’s contributionLinked views connect patterns to source excerpts, supporting selection, filtering, evidence inspection and human validation.
Theo’s contributionQualitative analysis involves more than assigning a label to a passage. Researchers develop categories, consider context and continually revisit the evidence behind an interpretation.
L4QA investigates where LLM workflows can help, how specialised agents change the results, and how linked views keep researchers involved.
Assess LLM workflows for Grounded Theory coding and multi-level sentiment analysis.
Examine the strengths and trade-offs of single-pass and multi-agent workflows.
Investigate linked visualisations for interpretation, traceability and human validation.
Select a topic to see how a view can connect an interpretation with the words that support it.
The selection changes the linked excerpt and interpretation together.
“The first steps were clear, but I still wanted an example before coding on my own.”
Clear instructions can coexist with a need for guided practice. The source contains both views.
This small interaction demonstrates the linking concept. It is not the research application and does not run an LLM.
The project’s results make a case for targeted assistance and careful review. They do not show a uniform improvement on every measure.
Up from 3.0% for the baseline. Code loss decreased, while semantic violations increased.
See the complete comparisonTerm micro-F1 rises from 41.12% to 45.32%. Document sentiment macro-F1 declines slightly.
Explore the sentiment resultsTasks completed in a five-participant study. Mean error-spotting confidence was 2.8/5.
Read the study findingsSelected findings from the three final papers. Each contribution includes the study context, limitations and full paper.
Each individual contribution has its own research focus, method, evaluation and supporting documents.
Grounded Theory
Read individual contributionSentiment Analysis
Read individual contributionVisualisations
Read individual contributionProject documents, final papers and website source code in one place.