Auto-code your open-ended responses

No more reading verbatims line by line: DataLion classifies every open-ended answer by AI sentiment and topic — and writes the results back as new variables you crosstab, weight and track like any other. Ready-made report included.

DataLion report with a sentiment donut and topic ranking from an open-ended survey question (n=220)

DataLion auto-codes open-ended responses with AI: each answer is classified by sentiment (binary, 3- or 5-point, or custom labels such as detractor/passive/promoter) and an auto-discovered topic. The results land as three new variables — sentiment, sentiment score and topic — directly in your dataset, ready to crosstab, weight, significance-test and track across waves.

  • 🇩🇪 Made in Munich
  • GDPR-compliant
  • DPA included
  • Hosted in Germany

Trusted by research, insights & media teams

  • YouGov
  • SevenOne Media
  • Mediengruppe RTL Deutschland
  • Nielsen Sports
  • mobile.de
  • 50+ interactive chart types
  • 20+ statistical methods
  • SPSS · Excel · CSV import without data loss
  • ISO 27001 certified data centers (Germany)

Open text is the most expensive question on the survey

Open ends are often the most valuable answers — and the ones that sit untouched the longest. Coding them by hand is slow, costly and barely consistent across coders. A word cloud shows the most frequent terms, but not whether they are meant positively or negatively.

DataLion does the coding for you: pick an open-text variable and the AI classifies every single answer by sentiment and topic — turning thousands of verbatims into a structured, analyzable dataset in minutes.

  • No more reading line by line
  • Consistent coding instead of coder drift
  • Sentiment plus topic — not just word frequency
  • Thousands of open ends per run
Word cloud of open-ended responses — most frequent terms by size, but without sentiment

Pick a scale, discover topics, run

You choose the open-text variable and a sentiment scale: binary (negative/positive), 3-point (negative/neutral/positive) or 5-point (very negative … very positive). Or supply your own ordered labels — for instance the NPS logic detractor, passive, promoter. Each class automatically gets a code and a numeric score.

For topics, you have the choice: DataLion discovers them automatically from a sample of your responses (up to 8, configurable) — or you supply your own codeframe (e.g. "pricing, support, usability") that is then used verbatim. An Other bucket catches anything that does not fit.

  • Sentiment scale binary / 3-point / 5-point
  • Or custom labels ordered from negative to positive
  • Discover topics automatically or supply a codeframe
  • Topics come back in the language of the responses
DataLion "Sentiment & topic analysis" setup dialog: open-text variable, sentiment scale, custom labels, maximum number of topics and predefined topics

Three new variables — not just a pretty chart

Instead of an isolated text report, DataLion writes the results back as three real variables in your dataset: a sentiment (categorical, coded and labeled), a numeric sentiment score (averageable) and the topic (categorical). They live in the codebook like any other variable.

So the coded sentiment flows straight into your usual analysis: crosstabs (sentiment × audience), weighting, significance tests, top/bottom-box, trackers across waves and native PowerPoint export. DataLion also builds a ready-made report with a sentiment chart and a topic chart — pair it with the word cloud for the same question.

  • Sentiment (categorical) + sentiment score (numeric) + topic (categorical)
  • Crosstab sentiment by segment and significance-test it
  • Track across waves — is the mood shifting?
  • Ready-made report included, native PowerPoint export
DataLion data table with the new variables FEEDBACK_SENTIMENT, FEEDBACK_SENTIMENT_SCORE and FEEDBACK_TOPIC next to the open-text answer

From NPS verbatims to brand trackers

The method fits anywhere open text shows up: the "why?" behind your NPS, open ends in brand tracking, customer feedback, employee surveys or product reviews. With custom labels you map any scale that fits your study.

Framed honestly: classification runs through an LLM (large language model). The LLM endpoint is configurable — for full data control you can point it at a self-hosted, EU or on-premise model rather than an external service. The DataLion platform and your data sit in ISO 27001-certified data centers in Germany. The analysis currently supports survey-collected datasets.

  • NPS and CX verbatims, brand tracking, HR and product feedback
  • Custom labels for any scale logic
  • Configurable LLM endpoint (self-hosted / EU / on-premise)
  • Platform hosted in Germany, GDPR-compliant

See DataLion with your own data

Start a free trial or book a personal demo — from raw data to a finished dashboard.

Top rated

4.5 out of 5 stars on G2 and OMR Reviews

What users say about DataLion

  • Very professional company, attentive to the customer needs, provider of a great software and service.
    Generoso M. CRM Analyst · Automotive via G2
  • The contacts at DataLion are very committed. If you have problems, you can count on help. DataLion reacts quickly to requests for new functions.
    Robert Q. Managing Director via G2
  • User-friendliness, especially for market research topics. Structured backend with many customization options.
    Verified user Market Research via G2
  • The embedding function allows us to generate insights of our data for our audience and customers by far less than half of the usual time needed before.
    Verified user Leisure, Travel & Tourism via G2
Read all 16 reviews on G2 →
We now work much more efficiently, giving us more time to take care of the derivations and insights from the data for the customers.
Jens Falkenau, Vice President of Market Research · Nielsen Sports
Read the case study →

More analysis features

Common questions about sentiment & topic analysis

How does DataLion code open ends?
You pick an open-text variable; an AI classifies each answer by sentiment and topic. Identical answers are classified once and propagated to all matching rows. The results are written back as three new variables — sentiment, sentiment score and topic — plus a ready-made report.
Which sentiment scales are available?
Binary (negative/positive), 3-point (negative/neutral/positive) and 5-point (very negative to very positive). Alternatively you supply your own labels ordered from negative to positive — such as detractor, passive, promoter for NPS logic. Each class automatically gets a code and a numeric score.
Are topics discovered automatically?
They can be: DataLion discovers recurring topics automatically from a sample of your responses (up to 8, configurable). You can also supply your own codeframe, which is then used verbatim. An Other bucket catches anything that does not fit.
What can I do with the results?
Sentiment, sentiment score and topic are full variables in the codebook. You crosstab them by segment, weight them, significance-test them, build top/bottom-box, track them across waves and export them natively to PowerPoint.
Where does my open text go during the AI analysis?
Classification runs through an LLM (large language model). The LLM endpoint is configurable: for maximum data control you can point it at a self-hosted or EU/on-premise model rather than an external service. The DataLion platform and your data sit in ISO 27001-certified data centers in Germany, GDPR-compliant.

Auto-code your open ends

Try DataLion free: sentiment and topics from open text via AI — as analyzable variables, not just a picture. Or book a personal demo.