Find the information that matters using natural language processing (NLP). 

Augmenting human efforts to analyze unstructured text with AI by providing a variety of modeling approaches that combine the power of natural language processing, machine learning and linguistic rules.  

Data preparation & visualization

Accesses, integrates, profiles, cleanses and transforms data. Imports text from more than 35 data connectors. Includes self-service data visualization for exploring and understanding text data.


Separates text into words, phrases, punctuation marks and other elements of meaning to provide the human framework a machine needs to analyze text at scale.

Trend analysis

Uses unsupervised machine learning to group documents based on common themes. Relevance scores calculate how well each document belongs to each topic, and a binary flag shows topic membership above a given threshold.

Information extraction

Pulls out specific pieces of information or relationships between information from text using a powerful, flexible and scalable SAS proprietary programming language called language interpretation for textual information (LITI).  

Hybrid modeling approaches

Combines a variety of capabilities needed to build effective text models, including a rich mix of linguistic rules, natural language processing, machine learning and deep learning.

Sentiment analysis

Identifies subjective information in text; labels it as positive, negative or neutral; associates that information with an entity; and provides a visual depiction through a sentiment indicator display.  

Flexible deployment

Deploy models in batch, Hadoop, in stream and via APIs. Score code is natively threaded for distributed processing, taking maximum advantage of computing resources to reduce latency to results.

Native support for 33 languages

Provides out-of-the-box NLP functionality to enable native language analysis using dictionaries and linguistic assets created by native language experts from around the world.

Open platform

Offers multithreaded parallel processing for in-memory analytics on a cloud ready, open architecture. REST APIs allow for flexible integration, and users have the choice to code in SAS, Python, R, Java, Scala or Lua.

Scale the human act of reading, organizing and extracting useful information from huge volumes of textual data.  

SAS Visual Text Analytics showing term map on desktop monitor

Detect emerging trends and hidden opportunities. 

Quickly and tirelessly sift through growing volumes of text data to identify main ideas or topics, extract key terms, analyze sentiment, and identify correlations between words with the right combination of natural language processing, machine learning and deep learning methods and linguistic rules. This helps get the right information to people when they need it.

Go from data to decisions faster. 

Empower decision making at the source of the data, and reduce the gap between when information is received and when it is acted on. If someone leaves a comment or clicks through an app on a mobile device, SAS Visual Text Analytics analyzes the data immediately using in-memory, in-database and in-stream technologies. Embedded visualization capabilities allow for visual exploration of both data and analytics, while also providing intuitive dashboards that easily communicate results to a variety of stakeholders.

SAS Visual Text Analytics showing ability to explore and visualize data on desktop monitor
SAS Visual Text Analytics showing pipelines on desktop monitor

Foster collaboration and information sharing in an open ecosystem.

SAS Visual Text Analytics provides a flexible environment that supports the entire analytics life cycle – from data preparation, to discovering analytic insights, to putting models into production to realize value. Create, manage and share content, including best practice pipelines, in a highly collaborative workspace that easily integrates with existing systems and open source technology.

Improve analytic workflow with automation.

Intelligent algorithms and NLP techniques automatically detect relationships and sentiment in text data, eliminating time-consuming manual analysis. The use of human subject matter expertise to refine results is augmented with automatic rule generation and an interactive sandbox that allows you to evaluate subsets of rules to determine which ones are better performing. Drag and drop functionality, best practice templates, simple merge and split features, effortless topic promotion, automatic rule generation and one-click model deployment collectively reduce the human model building effort required, creating more time to focus on finding the information that matters.

SAS Visual Text Analytics showing topics promoted to categories on desktop monitor

Get to Know SAS® Visual Text Analytics


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