Text data can be used for gaining insight into customer attitudes, routing communications from service representatives, finding key ‘nuggets’ and trends in news, emails and social media, and matching name and address data.

Aster’s text analytical functions, fully integrated with SQL, do rapid in-database analytics directly on very large sets of text data. The functions include document classification, named-entity recognition, sentiment extraction, matching identities with exact and fuzzy matching, among others. Each text function uses database tables as input and output. Organizations have used Aster text analytics to rapidly classify news stories, accurately extract ‘noisy’ content from OCRed text, match large  sets of names and addresses, find trending terms, cluster large sets of documents, and tie ‘star’ ratings to specific issues in customer comments. Users can filter the text analytics with structured data to focus the text analytics on a specific demographic.

Note: this is an expanded version of the 2014 Teradata Partners Conference session.

Presenter: Mark Turner - Teradata Corporation

This course is offered by the Teradata Education Network. To enroll online, click the Training URL link below to go to the TEN site and Log in. If you're not a member click browse, select your region, and search on the Course Number. Or to enroll by phone, call the Enrollment Center at 1-937-242-4460. Note: You must be a member to register for a course.
Course number:52911
Training URL:http://www.teradata.com/t/ten/
Format:Recorded webcast
Credit hours:1.25