Text Analytics for Business Decisions

A Case Study Approach

Mixed media product
May 2021
9781683926665
More details
  • Publisher
    Mercury Learning and Information
  • Published
    28th May
  • ISBN 9781683926665
  • Language English
  • Pages 318 pp.
  • Size 7" x 9"
$49.95
E-Book (ePub)
May 2021
9781683926641
More details
  • Publisher
    Mercury Learning and Information
  • Published
    13th May
  • ISBN 9781683926641
  • Language English
  • Pages 318 pp.
  • Size 7" x 9"
$39.95
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May 2021
9781683926658
More details
  • Publisher
    Mercury Learning and Information
  • Published
    13th May
  • ISBN 9781683926658
  • Language English
  • Pages 318 pp.
  • Size 7" x 9"
$129.95

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.

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

Andres Fortino

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.

Business Communication; Data Analytics; Computer Science; Data Visualization; R; Excel