Skip to content

Welcome to ThemeFinder

ThemeFinder is a topic modelling Python package designed for analyzing one-to-many question-answer data (i.e. survey responses, public consultations, etc.).

Quickstart

See README for how to get started with this package.

ThemeFinder pipeline

ThemeFinder's pipeline consists of five distinct stages, each utilizing a specialized LLM prompt:

Sentiment analysis

  • Analyses the emotional tone and position of each response using sentiment-focused prompts
  • Provides structured sentiment categorisation based on LLM analysis

Theme generation

  • Uses exploratory prompts to identify initial themes from response batches
  • Groups related responses for better context through guided theme extraction

Theme condensation

  • Employs comparative prompts to combine similar or overlapping themes
  • Reduces redundancy in identified topics through systematic theme evaluation

Theme refinement

  • Leverages standardisation prompts to normalise theme descriptions
  • Creates clear, consistent theme definitions through structured refinement

Theme mapping

  • Utilizes classification prompts to map individual responses to refined themes
  • Supports multiple theme assignments per response through detailed analysis

The prompts used at each stage can be found in src/themefinder/prompts/.

The file src/themefinder.core.py contains the function find_themes which runs the pipeline. It also contains functions for each individual stage.