Letter to the Reader
Why I wrote this book

Part 1: Text Mining Fundamentals and Core

Chapter 1: Comparison of Traditional Text Analysis Methods and Text Mining

Chapter 2 Text Mining, Text, and Corpora
1.
Definition and Usefulness of Text Mining
2.
The importance of text
3.
corpus

Chapter 3 Text Data
1.
Text data and natural language
2.
Structure of text data

Chapter 4 Preprocessing for Text Mining
1.
The need for preprocessing text data
2.
Remove format information from a document
3.
Distinction and unification of uppercase and lowercase letters
4.
Blank processing
5.
Spacing
6.
Numeric expression processing
7.
Remove punctuation and special characters
8.
Remove stop words
9.
Root identification
10.
Applying n-grams

Part 2 Text Mining Techniques and Practice

Chapter 5 Main Preprocessing Steps
1.
Tokenization
2.
Text preprocessing using regular expressions
3.
Remove stop words
4.
Preprocessing in practice through integrated examples

Chapter 6 Word Frequency Analysis
1.
definition
2.
purpose
3.
Key Terms
4.
How to present results
5.
Note
6.
Word frequency analysis flowchart
7.
Word Frequency Analysis Python Practice Example
8.
Word Frequency Analysis: Actual Paper Cases

Chapter 7 Topic Modeling
1.
definition
2.
purpose
3.
Algorithm
4.
How to present results
5.
Note
6.
Topic Modeling Flowchart
7.
Topic Modeling Python Practice Example
8.
Topic Modeling Real-World Paper Cases

Chapter 8 Cluster Analysis
1.
definition
2.
purpose
3.
Algorithm
4.
How to present results
5.
Note
6.
Cluster analysis flowchart
7.
Cluster Analysis Python Practice Example
8.
Cluster analysis actual paper examples

Chapter 9 Sentiment Analysis
1.
definition
2.
purpose
3.
Algorithm
4.
How to present results
5.
Note
6.
Sentiment Analysis Flowchart
7.
Sentiment Analysis Python Practice Example
8.
Sentiment Analysis Real-World Paper Case Studies

Chapter 10: Related Word Analysis
1.
definition
2.
purpose
3.
Algorithms and Key Terms
4.
How to present results
5.
Note
6.
Related word analysis flowchart
7.
Python practical example of association analysis
8.
Associated Word Analysis Real-World Paper Case Studies

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"What's more important than theory is the experience of analyzing it right now."

Many data analysis books claim to be "understandable even for beginners," but when you actually open the book, complex formulas and calculations appear as a barrier to entry.
But what readers really want to know are practical questions: “Which analysis method is right for my data?” and “How do I apply and interpret that analysis method?”
"Text Mining: Learning Through Case Studies" is a book that answers these practical questions.
Complex mathematical concepts are boldly excluded, and the focus is on fostering practical skills in analyzing and interpreting text data through real-world research examples, flowcharts visualizing analysis procedures, and Python practice code that can be copied and immediately executed.
This book provides beginners with their first successful experience in analysis, experienced practitioners with an opportunity to organize concepts and compare techniques, and educators with specific guidelines for teaching and practice.

- "Practical, structured content that can be immediately applied to reports and papers."

Above all, this book is a practical analysis book that covers everything from the core concepts of text mining to major analysis techniques in a consistent structure.
It consists of 10 chapters in 2 parts. Part 1 covers the basic theories such as the definition of text mining, corpus concept, and preprocessing procedures in detail. Part 2 presents core techniques such as word frequency analysis, topic modeling, cluster analysis, sentiment analysis, and related word analysis in the same format of 'definition-purpose-analysis flowchart-practice code-paper case study', designed to facilitate comparison and application between techniques.
All exercises are based on data and executable Python code used in actual research settings, helping you naturally acquire practical skills to go beyond simply learning concepts and experience and interpret the entire analysis process.
In particular, this book can be of practical help to the following readers:

For novice researchers who want to analyze text data themselves, this book serves as an introductory book that lowers the barrier to entry for analysis. For those who already know the techniques, this book serves as a compilation that organizes scattered knowledge and enables comparison between techniques. For educators and practitioners who need to teach text mining in classes or workshops, this book serves as a practical guidebook that can be used in actual educational settings.
Designed for readers of all skill levels to utilize in ways that suit them, this book is a practical and scalable text mining guide for both research and educational settings.