Contact us on (02) 8445 2300
For all customer service and order enquiries

Woodslane Online Catalogues

9781683926665 Add to Cart Academic Inspection Copy

Text Analytics for Business Decisions

A Case Study Approach
Table of
With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today's most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later Uses Excel and R for datasets in case studies and exercises Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining Companion files with numerous datasets and figures from the text
Andres Fortino, PhD holds an appointment as a clinical associate professor of management and systems at the NYU School of Professional Studies, where he teaches courses in business analytics, data mining, and data visualization. He also leads his own consulting company, Fortino Global Education. Dr. Fortino has published ten books and over 40 academic papers, and has received IBM's First Invention Level Award for his work in semiconductor research. He holds three US patents and ten invention disclosures.
1: Framing Analytical Questions. 2: Analytical Tool Sets. 3: Text Data Sources and Formats. 4: Preparing the Data File. 5: Word Frequency Analysis. 6: Keyword Analysis. 7: Sentiment Analysis. 8: Visualizing Text Data. 9: Coding Text Data. 10: Named Entity Recognition. 11: Topic Recognition in Documents. 12: Text Similarity Scoring. 13: Analysis of Large Datasets by Sampling. 14: Installing R and RStudio. 15: Installing the Entity Extraction Tool. 16: Installing the Topic Modeling Tool. 17: Installing the Voyant Text Analysis Tool. Index.
Google Preview content