Transform text data into actionable insights with Displayr’s AI-powered text analysis.
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.
Squeeze more meaning from your text analysis 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 is not just a text analytics app, it 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.
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.
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
A common example of text analysis is sentiment analysis, which uses machine learning and natural language processing to interpret large volumes of text data (emails, social media comments, reviews, etc.) and determine whether the text is negative, positive, or neutral. Learn more about how you can improve your sentiment analysis.
As well as sentiment analysis, some other examples of text analysis techniques include;
Text mining is another term for text analysis and refers to the process of analyzing textual data to glean insights. While ‘text analysis’ has been performed by humans for hundreds of years in fields like literature and social sciences, ‘text mining’ is a newer term that emerged from data mining and the inception of machine learning algorithms that automate text analysis. Explore the similarities and differences between text mining, text analysis, and text analytics.
Thematic coding is the process of finding common themes in text by analyzing, coding and identifying patterns in textual data. It goes beyond simply counting the most frequent words and phrases, but actually breaks down the data to highlight the relationships between different segments. This is how Displayr’s text analytics goes beyond surface-level keywords to understand the true meaning of the text. Learn more about how thematic coding powers text analysis.
Although similar, text analytics and natural language processing (NLP) are not the same. Text analytics provides actionable insights through statistical and machine learning techniques, while NLP is about processing, understanding, and generating human language (think Siri or Alexa). Displayr utilizes NLP to improve the quality of text analytics, allowing you to use prompts to ask highly detailed questions of your data.
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