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.
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.
Capture the context and meaning of words in a text to improve accuracy compared with traditional models. In addition to general classification, the BERT-based classification can be used to do sentiment analysis.
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.
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.
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.
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.
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.
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.
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.
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.
Available on Your Preferred Cloud Provider
Conquer all your analytics challenges with faster decisions in the cloud.
Check out these products and solutions related to SAS Visual Text Analytics.
- SAS® Data PreparationQuickly prepare data for analytics in a self-service, point-and-click environment with data preparation from SAS.
- SAS® Enterprise Miner™Streamline the data mining process to create highly accurate predictive and descriptive models based on large volumes of data.