TAYLLORCOX > Akademie > SAS > SAS(R) Contextual Analysis: Building Models

SAS(R) Contextual Analysis: Building Models

This course describes the functionality of SAS Contextual Analysis, a single web application that brings together the techniques used in text mining, categorization, contextual extraction, and sentiment identification. SAS Contextual Analysis helps you derive key business insight from unstructured text. It uses a combination of machine learning, linguistic, and subject matter expertise to uncover trends and themes in your data--insights that otherwise would have stayed hidden but can now give you an in-depth understanding of your business.

Termíny
Target group
Course structure

Virtual Training nebo e-Learning?

Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.

Zkuste živý kurz virtuálně

Target group

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

Course structure

Introduction to SAS Contextual Analysis

Introduce SAS Text Analytics.
Describe the tasks that can be performed using SAS Contextual Analysis.

Main Features of SAS Contextual Analysis

Create projects.
Extract terms using default concepts.
Extract system-generated topics.
Extract rules to identify that documents belong to categories.

SAS Contextual Analysis Demonstrations

Create a project with no predefined concepts.
Open a previously created project with custom concepts.

Advanced Features of SAS Contextual Analysis

Explore a SAS Contextual Analysis project created from a SAS Sentiment Analysis Studio SAM file.
Add custom concepts by importing concept definitions from an Enterprise Content Categorization TK2 file.

Concept and Category Rule Definitions in SAS Contextual Analysis

Introduce SAS Contextual Analysis rules.

Prerequisites

Experience in SAS programming or statistical knowledge is not required to attend. You should be able to log on and off a computer, use a keyboard and mouse, and have a preliminary understanding of the differences between structured (numeric) and unstructured (text) data fields.