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SAS® Text Analytics reveals new insights in stored documents

          

SAS Text Analytics, now shipping from the leader in business analytics, automates the time-consuming process of reading individual documents and manually extracting relevant information

SAS Text Analytics, now shipping from the leader in business analytics, automates the time-consuming process of reading individual documents and manually extracting relevant information. Organizations are in constant need of powerful tools to manage proliferating data from social networks, call centers, customer surveys, claims forms and sales returns. SAS' award-winning analytics mine, interpret and structure information to reveal patterns, sentiment and relationships to improve decision making.

Four SAS Text Analytics offerings

SAS suite harnesses the power and expertise of the Teragram technologies acquired in 2008:

SAS Enterprise Content Categorization applies natural language processing and advanced linguistic techniques to automatically categorize multilingual content. It parses and analyzes content for entities, facts and events to create metadata, develop taxonomies, and generate category rules and concept definitions to apply to large volumes of documents to trigger business processes.

SAS Sentiment Analysis derives positive and negative opinions, evaluations and emotions of customers and prospects from digital content sources, including blogs, tweets and Internet sites, as well as internal e-mails, call center notes and inquiries.

SAS Text Miner incorporates advanced linguistics into SAS core data mining solution, SAS Enterprise Miner™. Consolidating structured data analysis with unstructured text provides more meaningful insights from predictive modeling. Automating manual exploration of text, incorporating interactive, drill-down reporting and delivering algorithms for rigorous advanced analyses help organizations grasp future trends and act on new opportunities more efficiently and with less risk.

SAS Ontology Management creates and maintains consistent and centralized metadata across document collections and textual repositories, so information search-and-retrieval engines can systematically identify common concepts. This provides meaningful responses to complex questions, even when answers are not explicitly stated in the text.

For more information, see

www.sas.com/text-analytics/index.html


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