Combine machine learning with subject-matter expertise to identify trends and topics within unstructured data. You guide the technology to define categories that are important to your organization, and bring order to the chaos of free-format text.
Classify documents for better retrieval and reporting.
SAS Contextual Analysis allows you to easily structure free-format text for classifying documents and for use in interactive visual analytics and reporting.
Eliminate lengthy, error-prone manual reviews.
Applying derived text models to documents removes the inconsistency of manual tagging so you can process higher volumes of information with better quality. And reduce the opportunity costs of using valuable subject-matter experts for standard information tasks.
Automate trend discovery and classifications.
Instead of a manual process of defining a training corpus to initiate model development, SAS Contextual Analysis provides an automated method to define themes in unstructured data. This gives you a faster, more accurate start to understanding document collections.
Refine rules to enhance text analysis precision.
A single interface allows you to review categories identified by machine learning. Then you can edit, augment and clarify these identified rule models and validate these changes, testing them against validation samples.
Add unstructured data to analytics investigations.
SAS Contextual Analysis provides the ability to structure documents, emails, books and other text sources to find key categories within text information – and formats scored data for use directly in SAS Visual Analytics or other SAS applications.
- Integrated system that guides categorization model development
- Hybrid approach to classifying documents
- Direct integration with SAS®
- Natural language processing (NLP)
- Automatic discovery of topics
- Configurable categorization rule generation