Text Analytics for Business Decisions

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