SAS(R) Visual Text Analytics in SAS(R) Viya(R)

Course code: SVTA35

SAS Visual Text Analytics enables you to uncover insights hidden within unstructured data using the combined power of natural language processing, machine learning, and linguistic rules. This course explores the five components of Visual Text Analytics: parsing, concept derivation, topic derivation, text categorization, and sentiment analysis. Documents are parsed and analyzed to reveal dominant themes in the document collection. Sophisticated linguistic queries are constructed to satisfy specific information needs. An integrated solution is developed using information extracted from subject matter expert rules, combined with machine learning results for model and rule-based topics and categories. The course includes hands-on use of SAS Viya in a distributed computing environment.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 14 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

Text analysts, business and marketing analysts, web analysts, BI professionals, customer intelligence professionals, social media analysts, and document librarians

Course structure

Introduction to SAS Visual Text Analytics

  • Introduction.
  • Language challenges (self-study).

SAS Visual Text Analytics Demonstrations

  • Importing document collections.
  • Creating a project with no predefined concepts.
  • A project with custom concepts.

SAS Visual Text Analytics Nodes

  • Introduction.
  • Concepts and terms.
  • Machine-generated topics.
  • Categories.
  • Scoring new documents.

Concept and Category Rule Definitions

  • SAS Visual Text Analytics rules.
  • SAS Visual Text Analytics concept rules.
  • SAS Visual Text Analytics demo category rules.

Case Studies

  • Retrieving information and documents about anxiety and depression from drug reports.
  • Automatic categorization of ASRS incident reports.
  • Retrieving mortgage complaints from the CFPB customer complaints data (self-study).


Neither SAS programming experience nor statistical knowledge is required. You should be comfortable using a computer, have experience using browser-based software solutions, and have a basic understanding of the differences between structured (numeric) and unstructured (text) data fields.

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