Get maximum value from your unstructured data using a wide variety of modeling approaches – including supervised and unsupervised machine learning, linguistic rules, categorization, entity extraction, sentiment analysis and topic detection. SAS Visual Text Analytics helps you overcome the challenges of identifying and categorizing large volumes of text data.
Demo
See SAS® Visual Text Analytics in action.
SAS Visual Text Analytics combines natural language processing, machine learning and linguistic rules to help you uncover emerging trends, spot opportunities for action, and unlock the true value of all your unstructured text data. Our end-to-end analytics framework enables you to prepare data, dynamically explore and visualize text, as well as build and deploy a variety of text analytics models.
Key Features
- Combined machine-learning and rules-based methods. Automatically identify core themes in a collection of documents using machine-learning techniques, and apply linguistic rules to understand slang and infer intention. Create precise, rules-based categories and concepts when discovered themes need additional tuning or custom definitions.
- Contextual extraction. Detect and extract data elements and relationships from unstructured text with a comprehensive toolset that includes predefined concepts, as well as the ability to create custom concepts and definitions.
- Flexible deployment. Deploy models in batch, in Hadoop, in stream and via APIs. Run models closer to where data is collected to reduce data movement and produce faster results for scoring new data.
- Multiuser environment. The SAS Platform offers more depth and breadth of integrated analytical capabilities than any other software. This integration encourages teamwork and collaboration by providing a workspace for sharing best-practice pipelines and methods.
- Natural language processing. Automatically analyze and transform text into formal representations for text processing and understanding using NLP. Natural language understanding (NLU), a subset of NLP, enables contextual understanding of content.
- Automated machine-generated topic detection. Derive topics automatically from your documents using two unsupervised machine-learning methods – singular value decomposition and latent Dirichlet allocation.
- Native support for 30 languages. Eliminate the need to translate language prior to analysis with proprietary language packs that enable native language analysis using dictionaries and linguistic assets created by native language experts.
- Sentiment analysis. Identify and analyze terms, phrases and character strings that imply an author’s tone or attitude (positive, negative or neutral) expressed through text.
- Open APIs. Seamlessly integrate with existing systems and open source technologies, and add the power of SAS Analytics to other applications using SAS® Viya® REST APIs.
This solution runs on SAS® Viya®, which has the breadth and depth to conquer any analytics challenge, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.
Recommended Resources
Discover what text analytics can do for your organization in this research brief from the International Institute for Analytics.
