Kwartler Ted. Text Mining in Practice with R

John Wiley & Sons, 2017. — 312 p. — ISBN: 9781119282099.A reliable, cost-effective approach to extracting priceless business information from all sources of text.Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R.Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modeling, predictive modeling, extracting clickbait from headlines, and more.What is Text Mining?
What is it?
Why We Care About Text Mining
A Basic Workflow – How the Process Works
What Tools Do I Need to Get Started with This?
A Simple Example
A Real World Use CaseBasics of Text Mining
What is Text Mining in a Practical Sense?
Types of Text Mining: Bag of Words
The Text Mining Process in Context
String Manipulation: Number of Characters and Substitutions
Keyword Scanning
String Packages stringr and stringi
Preprocessing Steps for Bag of Words Text Mining
Spellcheck
Frequent Terms and Associations
DeltaAssist Wrap UpCommon Text Mining Visualizations
A Tale of Two (or Three) Cultures
Simple Exploration: Term Frequency, Associations and Word Networks
Simple Word Clusters: Hierarchical Dendrograms
Word Clouds: Overused but EffectiveSentiment Scoring
What is Sentiment Analysis?
Sentiment Scoring: Parlor Trick or Insightful?
Polarity: Simple Sentiment Scoring
Emoticons – Dealing with These Perplexing Clues
R’s Archived Sentiment Scoring Library
Sentiment the Tidytext Way
Airbnb.com Boston Wrap UpHidden Structures: Clustering, String Distance, Text Vectors and Topic Modeling
What is clustering?
Calculating and Exploring String Distance
LDA Topic Modeling Explained
Text to Vectors using text2vecDocument Classification: Finding Clickbait from Headlines
What is Document Classification?
Clickbait Case StudyPredictive Modeling: Using Text for Classifying and Predicting Outcomes
Classification vs Prediction
Case Study I: Will This Patient Come Back to the Hospital?
Case Study II: Predicting Box Office SuccessThe OpenNLP Project
What is the OpenNLP project?
R’s OpenNLP Package
Named Entities in Hillary Clinton’s Email
Analyzing the Named EntitiesText Sources
Sourcing Text
Web Sources
Getting Text from File Sources