Text Analytics Using SAS Text Miner



In this course, you will learn about the functionality of SAS Text Miner software, which is a separately licensed component that is available for SAS Enterprise Miner. You will also learn how to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections. Additionally, you will learn how to automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors.


  • Statisticians, business analysts, and market researchers who incorporate free-format textual information in their analyses
  • Managers of large document collections who must organize and select documents using data mining
  • Individuals who want to learn about text mining


  • Experience using SAS Enterprise Miner to do pattern discovery and predictive modeling (or you may complete the Applied Analytics Using SAS Enterprise Miner course in lieu of experience).
  • Familiarity with Microsoft Windows and Windows-based software
  • Familiarity with basic statistics and regression modeling
  • Previous SAS software experience (especially SAS Enterprise Miner) is helpful but not required

Learning Objectives

  • Process textual data and show how it can be used in predictive modeling and exploratory analysis
  • Convert unstructured character data into structured numeric data
  • Explore words and phrases in a document collection
  • Cluster documents into homogeneous subgroups
  • Find documents most closely associated with a word or phrase
  • Find words or phrases most closely associated with a document
  • Identify topics in a document collection
  • Classify documents based on derived or user-supplied topic definitions
  • Extract a subset of documents with term-based and string-based query filters and use textual data to improve predictive models

1. Introduction to SAS Enterprise Miner and SAS Text Miner

  • Data mining and text mining
  • Working with data sources
  • Using SAS Enterprise Miner and SAS Text Miner

2. Overview of Text Analytics

  • Using the Text Import node, adding a target variable, and comparing models
  • Forensic linguistics application
  • Information retrieval

3. Algorithmic and Methodological Considerations in Text Mining

  • Methods for parsing and quantifying text
  • Dimension reduction with SVD

4. Additional Ideas and Nodes

  • Some predictive modeling details
  • Text Rule Builder node
  • High Performance (HP) Text Miner node