Theme Finder

From raw text to themes

Thematic analysis is the most common method for qualitative data. It starts with reading the data, then coding it, then grouping codes into themes. The theme finder speeds up the first exploration by showing which words co-occur across responses, so you know where to begin. Candidate themes still need coding and interpretation, as in thematic analysis.

How the tool finds candidate themes

• Each line you paste is treated as one response.
• Common stop words are removed, and each remaining word is counted once per response.
• The most widespread word becomes the seed of a theme. Words that appear in the same responses are grouped with it.
• The tool then lists how many responses touch each theme and shows up to three example quotes.

What to do with the output

Treat the result as a draft codebook, not a finished analysis. Read the example quotes, rename each theme in meaningful language, merge overlapping themes and look for responses the tool missed. Record your decisions so that your analysis is transparent and replicable.

Getting good input

Use at least 10 responses, and keep each response on its own line. Remove interviewer questions and names. Short, focused answers work best. For long transcripts, split them into paragraphs first.

Going further

For large datasets, our team offers full qualitative coding, topic modelling and NLP. You can also check the language of each theme with the keyword in context viewer.

Related analysis services

Theme Finder gives you candidate themes to explore. Turn them into a coded framework with thematic analysis, grounded theory or interview and focus group analysis in NVivo, MAXQDA or ATLAS.ti, or discover themes in large text collections with topic modelling. See all services or tell us about your project.