This course covers the functionality of SAS Text Miner software, which is a separately licensed component available for SAS Enterprise Miner. In this course, you learn to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections, automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors.
Learn how to
- process and prepare textual data for analysis
- convert unstructured character data into structured numeric data
- explore words and phrases in a document collection
- group documents using similarity measures
- 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
- apply association discovery techniques to help understand the importance of noun phrases
- address problems from the areas of forensic linguistics, document categorization, and information retrieval
- use textual data to improve predictive models.
Who should attend
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; students of data mining who want to learn about text mining
Before attending this course, you should have experience using SAS Enterprise Miner to do pattern discovery and predictive modeling, or you should have completed the Applied Analytics Using SAS Enterprise Miner course.
A three-day version of this course contains the appropriate introductory material for using SAS Enterprise Miner. For the three-day course, you should also
- be acquainted with Microsoft Windows and Windows-based software
- have at least an introductory-level familiarity with basic statistics and regression modeling.
Previous SAS software experience is helpful but not required.
This course addresses SAS Text Analytics, SAS Text Miner software.
Introduction to SAS Enterprise Miner and SAS Text Miner
Overview of Text Analytics
- data mining and text mining
- SAS Enterprise Miner and SEMMA methodology (self-study)
- working with data sources
- using SAS Enterprise Miner and SAS Text Miner
- developing predictive models (self-study)
- discovering patterns in data (self-study)
Algorithmic and Methodological Considerations in Text Mining
- data preparation for text analytics
- forensic linguistics
- information retrieval
- text categorization
Applications of Text Mining to Pattern Discovery
- methods for parsing and quantifying text
- quantifying concepts using latent semantic analysis
Applications of Text Mining to Predictive Modeling
- exploring call center logs
- processing and categorizing documents
- nested clustering of warranty claims
- association and sequence discovery in text analytics
- using adjustor notes to predict recovery potential in insurance claims
- exploratory predictive modeling of the VAERS data