SAS® Contextual Analysis delivers faster insight from unstructured big data by automating text analysis
Industry’s leading advanced analytics also updated with customer-requested capabilities
New SAS® Contextual Analysis software eliminates tedious manual tagging and categorization for structuring unstructured data. For organizations craving big data insight from text such as social media or customer communications, that’s a huge time savings. SAS also added new upgrades across its advanced analytics portfolio.
SAS Contextual Analysis helps organizations discover emerging issues, patterns, and trends in unstructured data without requiring prior knowledge of its contents. Previously, in order to categorize content, users had to first define training sets and taxonomies, and then establish categorization rules.
“Traditionally, the most difficult part of uncovering value in text from social media, call center notes or warranty card comments, is first building a taxonomy," said Fiona McNeill, SAS Global Product Marketing Manager. “Typically this has been a lengthy manual process that involves defining concepts and categories that describe a document collection.”
“Until a taxonomy has been created, the text data can’t be assessed, so taxonomies are essential to provide business value. With SAS Contextual Analysis, we’ve now automated much of this process virtually eliminating the burden of manual taxonomy development,” she said.
The SAS Contextual Analysis point-and-click interface guides data analysts through text model development, providing initial taxonomy development and defining taxonomy rules from raw text inputs using patented automatic linguistic rules creation techniques.
"Text analytics adopters are looking for faster time-to-insight and greater accessibility for non-data scientist users, without sacrificing analytical sophistication or performance," said Seth Grimes, an industry analyst and consultant with Alta Plana Corp. and noted text analytics authority. "SAS Contextual Analysis is well positioned to meet this need, with an added ability to jump-start analyses via machine learning that should be well received by the business market."
With complete access to the machine-generated topics, rules, and concepts, data analysts can further refine automatically generated results using a vast suite of prebuilt linguistic operators. While suitable for new and experienced text analysts, SAS also includes interactive visual graphics, sentiment and diagnostic metrics for extending machine automatically generated text models.
“A powerful, guided methodology anchors SAS Contextual Analysis, combining machine learning techniques with end-user subject-matter expertise to largely automate categorization model development,” said McNeill. “SAS Contextual Analysis represents a new era of semantic analysis, helping organizations gain rich insights from their text data while significantly simplifying the model development process for categorizing content in real time.”
Building on SAS’ innovation in text and sentiment analytics, this new software simplifies integration of unstructured data into other SAS applications, such as SAS Visual Analytics for interactive visual data exploration and reporting. Existing SAS Enterprise Content Categorization projects can also be imported to SAS Contextual Analysis. Only SAS Text Analytics provides:
- Unstructured data analytics built on the world’s most trusted analytics.
- Multicore processor options for lightning-fast analysis.
- Integrated natural language processing, spell checking, synonym detection and document conversion.
The best gets better
Already the advanced analytics market leader, according to IDC, SAS software provides greater accuracy, ease of use and options with its latest enhancements. Across a broad range of analytic functions and distinct offerings, SAS maintains its leadership by reinvesting 25 percent of annual revenue in R&D.
“SAS has always created software our customers ask for. SAS Contextual Analysis and hundreds of new enhancements and procedures in our SAS advanced analytics suite prove yet again that we’re committed to our customers,” said Radhika Kulkarni, Vice President of Advanced Analytics Research and Development at SAS.
SAS Enterprise Miner™ now integrates open source analytics through an embedded node, letting users put R language code directly inside a process-flow diagram. This boosts the productivity of data scientists who operate across several analytics platforms. And SAS® Text Miner includes a new text profiling capability, to provide even deeper insight into term trends that can improve SAS Enterprise Miner model lift.
Meeting the increased need for real-time analytics, SAS Event Stream Processing Engine continuously analyzes data as it is received. This powerful software includes new adapters to load data to Hadoop, Teradata and other systems, in addition to categorization of streaming text data.
SAS/STAT®, SAS Forecast Server, SAS/ETS®, SAS/IML® and other advanced analytics products have also undergone significant upgrades.
For help finding insight in text, please download this checklist report created by TDWI.
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The Categories page in SAS Contextual Analysis displays promoted topics along with the component rules and qualifying documents for the selected category.