Text Analytics for Business Decisions: A Case Study Approach

✍ Scribed by Andres Fortino

• Publisher: Mercury Learning and Information
• Year: 2021
• Tongue: English
• Leaves: 333
• Category: Library

✦ Synopsis

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.\n\nFEATURES:\n\n Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later\n\n Uses Excel and R for datasets in case studies and exercises\n\n Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining\n\n Companion files with numerous datasets and figures from the text.

✦ Table of Contents

Contents
Preface
On the Companion Files
Acknowledgements
Chapter 1 : Framing Analytical Questions
Data is the New Oil
The World of the Business Data Analyst
How Does Data Analysis Relate to Decision Making?
How Do We Frame Analytical Questions?
What are the Characteristics of Well-framed Analytical Questions?
Exercise 1.1 - Case Study Using Dataset K: Titanic Disaster
What are Some Examples of Text-Based Analytical Questions?
Additional Case Study Using Dataset J: Remote Learning Student Survey
References
Chapter 2 : Analytical Tool Sets
Tool Sets for Text Analytics
Excel
Microsoft Word
Adobe Acrobat
SAS JMP
R and RStudio
Voyant
Java
Stanford Named Entity Recognizer (NER)
Topic Modeling Tool
References
Chapter 3 : Text Data Sources and Formats
Sources and Formats of Text Data
Social Media Data
Customer opinion data from commercial sites
Email
Documents
Surveys
Websites
Chapter 4 : Preparing the Data File
What is Data Shaping?
The Flat File Format
Shaping the Text Variable in a Table
Bag-of-Words Representation
Single Text Files
Exercise 4.1 - Case Study Using Dataset L: Resumes
Exercise 4.2 - Case Study Using Dataset D: Occupation Descriptions
Additional Exercise 4.3 - Case Study Using Dataset I: NAICS Codes
Aggregating Across Rows and Columns
Exercise 4.4 - Case Study Using Dataset D: Occupation Descriptions
Additional Advanced Exercise 4.5 - Case Study Using Dataset E: Large Data Files
Additional Advanced Exercise 4.6 - Case Study Using Dataset F: The Federalist Papers
References
Chapter 5 : Word Frequency Analysis
What is Word Frequency Analysis?
How Does It Apply to Text Business Data Analysis?
Exercise 5.1 - Case Study Using Dataset A: Training Survey
Exercise 5.2 - Case Study Using Dataset D: Job Descriptions
Exercise 5.3 - Case Study Using Dataset C: Product Reviews
Additional Exercise 5.4 - Case Study Using Dataset B: Consumer Complaints
Chapter 6 : Keyword Analysis
Exercise 6.1 - Case Study Using Dataset D: Resume and Job Description
Exercise 6.2 - Case Study Using Dataset G: University Curriculum
Exercise 6.3 - Case Study Using Dataset C: Product Reviews
Additional Exercise 6.4 - Case Study Using Dataset B: Customer Complaints
Chapter 7 : Sentiment Analysis
What is Sentiment Analysis?
Exercise 7.1 - Case Study Using Dataset C: Product Reviews - Rubbermaid
Exercise 7.2 - Case Study Using Dataset C: Product Reviews-Windex
Exercise 7.3 - Case Study Using Dataset C: Product Reviews-Both Brands
Chapter 8 : Visualizing Text Data
What Is Data Visualization Used For?
Exercise 8.1 - Case Study Using Dataset A: Training Survey
Exercise 8.2 - Case Study Using Dataset B: Consumer Complaints
Exercise 8.3 - Case Study Using Dataset C: Product Reviews
Exercise 8.4 - Case Study Using Dataset E: Large Text Files
References
Chapter 9 : Coding Text Data
What is a Code?
What are the Common Approaches to Coding Text Data?
What is Inductive Coding?
Exercise 9.1 - Case Study Using Dataset A: Training
Exercise 9.2 - Case Study Using Dataset J: Remote Learning
Exercise 9.3 - Case Study Using Dataset E: Large Text Files
Affinity Diagram Coding
Exercise 9.4 - Case Study Using Dataset M: Onboarding Brainstorming
References
Chapter 10 : Named Entity Recognition
Named Entity Recognition
What is a Named Entity?
Common Approaches to Extracting Named Entities
Classifiers - The Core NER Process
What Does This Mean for Business?
Exercise 10.1 - Using the Stanford NER
Exercise 10.2 - Example Cases
Exercise 10.2 - Case Study Using Dataset H: Corporate Financial Reports
Additional Exercise 10.3 - Case Study Using Dataset L: Corporate Financial Reports
Exercise 10.4 - Case Study Using Dataset E: Large Text Files
Additional Exercise 10.5 - Case Study Using Dataset E: Large Text Files
References
Chapter 11 : Topic Recognition in Documents
Information Retrieval
Document Characterization
Topic Recognition
Exercises
Exercise 11.1 - Case Study Using Dataset G: University Curricula
Exercise 11.2 - Case Study Using Dataset E: Large Text Files
Exercise 11.3 - Case Study Using Dataset E: Large Text Files
Exercise 11.4 - Case Study Using Dataset E: Large Text Files
Exercise 11.5 - Case Study Using Dataset E: Large Text Files
Additional Exercise 11.6 - Case Study Using Dataset P: Patents
Additional Exercise 11.7 - Case Study Using Dataset F: Federalist Papers
Additional Exercise 11.8 - Case Study Using Dataset E: Large Text Files
Additional Exercise 11.9- Case Study Using Dataset N: Sonnets
References
Chapter 12 : Text Similarity Scoring
What is Text Similarity Scoring?
Text Similarity Scoring Exercises
Exercise 12.1 - Case Study Using Dataset D: Occupation Description
Analysis using R
Exercise 12.2 - Case D: Resume and Job Description
Reference
Chapter 13 : Analysis of Large Datasets by Sampling
Using Sampling to Work with Large Data Files
Exercise 13.1 - Big Data Analysis
Additional Case Study Using Dataset E: BankComplaints Big Data File
Chapter 14 : Installing R and RStudio
Installing R
Install R Software for a Mac System
Installing RStudio
Reference
Chapter 15 : Installing the Entity Extraction Tool
Downloading and Installing the Tool
The NER Graphical User Interface
Reference
Chapter 16 : Installing the Topic Modeling Tool
Installing and Using the Topic Modeling Tool
Install the tool
For Macs
For Windows PCs
UTF-8 caveat
Setting up the workspace
Workspace Directory
Using the Tool
Select metadata file
Selecting the number of topics
Analyzing the Output
Multiple Passes for Optimization
The Output Files
Chapter 17 : Installing the Voyant Text Analysis Tool
Install or Update Java
Installation of Voyant Server
The Voyant Server
Downloading VoyantServer
Running Voyant Server
Controlling the Voyant Server
Testing the Installation
Reference
INDEX