Displayr is the AI text analytics platform that turns open-ended text, from survey verbatims to online feedback, into themes and quantified insight, without coding.
Get accurate and consistent results that are better than manual analysis. Displayr uses advanced Artificial Intelligence (AI) and Natural Language Processing (NLP) to grasp the meaning behind your text data. The AI looks at the context and meaning – not just keywords – categorizing verbatims into reliable themes and insights every time.
Displayr is a text analysis tool designed to help you squeeze more meaning from your text data by asking highly detailed questions and using prompts to perform any type of text analysis including sentiment analysis, entity extraction, intention detection, topic modeling, emotion detection, categorization, and overlapping categorizations.
Displayr’s text tool is designed with a simple, user-friendly interface that anyone can use without technical skills. Start exploring and analyzing your text data without any hassle, saving you time and effort.
Displayr’s text analysis software does visualization, trend analysis, crosstabs, advanced analysis, dashboards, PowerPoint reporting, data apps, and more. Once you have categorized your text, you can easily integrate it into your report and even update everything automatically with new data.
Insights Professional
Template text categorizations, re-use your themes and rules to save time. Auto-update your analysis with new data.
Analyze text data in any language, with true native language support perfect for global organizations and diverse datasets.
Work faster with your team in the same document simultaneously, with version control, commenting and shared editing.
Displayr’s Research Agents integrate AI across your full workflow, from data cleaning to analysis to reporting.
AI automatically identifies and categorizes themes within your text data, providing deeper insights.
Understand and analyze complex emotions like frustration and sadness, helping you understand customer motives.
Extract key entities like names, places, and organizations to enrich your analysis.
Fine-tune and adjust categories to match your specific needs and preferences.
Create stunning word clouds, charts, and dashboards that help tell the story behind your text.
Analyze text data in any language, with true native language support to a global audience.
Analyze large volumes of text to gauge positive, negative, or neutral sentiments.
Extract insights with unrivalled accuracy, utilizing NLP to reduce manual effort and free up time.
Displayr helps Vennli complete projects 5x faster
Still weighing your options? See our roundup of the best AI text analysis tools to compare Displayr with the alternatives.
Market researchers use text analytics software to analyse open-ended survey responses at scale, online reviews, sentiment in customer feedback, and social comments — finding recurring themes and measuring sentiment across large volumes of text. Because Displayr quantifies the results, you can see not just what customers say but how many say it, turning qualitative feedback into data you can act on.
Displayr’s text analytics tool uses AI to read open-ended text, categorise it into themes, and analyse sentiment — in any language. It then quantifies those themes as scores you can crosstab, track over time, and drop straight into dashboards and reports. Everything happens in one platform, so you go from raw text to a finished result without exporting to another tool.
Yes. Displayr is a no-code text analytics platform — you can categorise open-ended responses, run sentiment analysis, and build themes without writing a line of code. You use plain-language prompts to guide the AI, so anyone on the team can run text analytics, not just data scientists. Advanced users can add R if they want, but it’s never required.
Text analytics software uses AI and natural language processing to analyse unstructured text – survey verbatims, reviews, social posts, open-ended feedback – and turn it into structured, measurable insight. Instead of reading and coding responses by hand, it automatically categorises text into themes, detects sentiment, and quantifies the results so you can analyse them like any other data.
