SAS Visual Text Analytics

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

Scale the human act of reading, organizing and extracting useful information from huge volumes of textual data with SAS Visual Text Analytics.

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.

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 analytics 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 shown on desktop monitor

Free Trial

Try the latest SAS Viya capabilities.

Get a free, 14-day trial of SAS Viya, which includes SAS Visual Text Analytics, as well as capabilities for the entire analytics life cycle.

Customer Success

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Key Features

Augment human efforts to analyze unstructured text with AI using a variety of modeling approaches. Experience the combined power of natural language processing, machine learning and linguistic rules.

Data access, preparation & quality

Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.

Custom chatbot creation

Create and deploy custom, natural language chatbots via an intuitive, low-code visual interface for chatbot-enabled insights and conversational user experiences​.

Data visualization

Visually explore data and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data​.

Parsing

Text is separated 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

Unsupervised machine learning groups 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

Pull 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

Build effective text models using a variety of combined capabilities, including a rich mix of linguistic rules, natural language processing, machine learning and deep learning.

Sentiment analysis

Subjective information is identified in text and labeled as positive, negative or neutral. That information is associated with an entity, and a visual depiction is provided through a sentiment indicator display.

Corpus analysis

Understand corpus structure through easily accessible output statistics to leverage natural language generation (NLG) for tasks such as data cleansing, separating out noise, sampling effectively, preparing data as input for further models (rules-based and machine learning), and strategizing modeling approaches.

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

Out-of-the-box NLP functionality enables native language analysis using dictionaries and linguistic assets created by native language experts from around the world.

Cloud native

SAS Viya's architecture is compact, cloud-native and fast. Whether you prefer to use the SAS Cloud or a public or private cloud provider, you'll be able to make the most of your cloud investment.

Recommended Resources

E-Book

Make Every Voice Heard With Natural Language Processing

Learn how machines are being taught to understand the nuances of human language, the role AI plays, and how multiple industries are delivering better products and services through natural language processing.

Solution Brief

Natural language processing for government efficiency

Discover how you can uncover meaningful insights and make faster, better decisions from vast amounts of text-based data.

Blog

SAS Blogs: Text Analytics

Stay up to date on the latest news and advancements in text analytics.

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