Introduction to Text Analytics

A Guide for Digital Humanities & Social Sciences
Emily Öhman - Waseda University, Japan
Introduction to Text Analytics
November 2024 | 360 pages | Sage UK
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Description


This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data.

It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach.

Written specifically for students and early career researchers this pragmatic manual will: 

•             Contextualise your learning with real-world data and engaging case studies.

•             Encourage the application of your new skills with reflective questions.

•             Enhance your ability to be critical, and reflective, when dealing with imperfect data.

Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. 

Contents

Basic Concepts and Tools for Text Analytics

  • Chapter 1: Computational and Traditional Text Analysis
  • Chapter 2: Basic Tools for Text Analytics
  • Chapter 3: Dataset Creation and Considerations

Language and Computers

  • Chapter 4: Language and Computers
  • Chapter 5: Regular Expressions

Programming for Text Analytics

  • Chapter 6: Introduction to Python Programming
  • Chapter 7: Pre-processing Textual Data
  • Chapter 8: Data Manipulation and Exploration
  • Chapter 9: Data Visualization

Social Media Analytics

  • Chapter 10: Text Mining
  • Chapter 11: Social Media Analysis
  • Chapter 12: The Basics of Machine Learning

Publishing

  • Chapter 13: LaTex Basics

Description


This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data.

It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach.

Written specifically for students and early career researchers this pragmatic manual will: 

•             Contextualise your learning with real-world data and engaging case studies.

•             Encourage the application of your new skills with reflective questions.

•             Enhance your ability to be critical, and reflective, when dealing with imperfect data.

Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. 

Contents

Basic Concepts and Tools for Text Analytics

  • Chapter 1: Computational and Traditional Text Analysis
  • Chapter 2: Basic Tools for Text Analytics
  • Chapter 3: Dataset Creation and Considerations

Language and Computers

  • Chapter 4: Language and Computers
  • Chapter 5: Regular Expressions

Programming for Text Analytics

  • Chapter 6: Introduction to Python Programming
  • Chapter 7: Pre-processing Textual Data
  • Chapter 8: Data Manipulation and Exploration
  • Chapter 9: Data Visualization

Social Media Analytics

  • Chapter 10: Text Mining
  • Chapter 11: Social Media Analysis
  • Chapter 12: The Basics of Machine Learning

Publishing

  • Chapter 13: LaTex Basics
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Introduction to Text Analytics

A Guide for Digital Humanities & Social Sciences

Emily Öhman
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November 2024 | 360 pages | Sage UK

Format Published Date ISBN Price


This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data.

It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach.

Written specifically for students and early career researchers this pragmatic manual will: 

•             Contextualise your learning with real-world data and engaging case studies.

•             Encourage the application of your new skills with reflective questions.

•             Enhance your ability to be critical, and reflective, when dealing with imperfect data.

Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. 


Table Of Contents:

  • Basic Concepts and Tools for Text Analytics
  • Chapter 1: Computational and Traditional Text Analysis
  • Chapter 2: Basic Tools for Text Analytics
  • Chapter 3: Dataset Creation and Considerations
  • Language and Computers
  • Chapter 4: Language and Computers
  • Chapter 5: Regular Expressions
  • Programming for Text Analytics
  • Chapter 6: Introduction to Python Programming
  • Chapter 7: Pre-processing Textual Data
  • Chapter 8: Data Manipulation and Exploration
  • Chapter 9: Data Visualization
  • Social Media Analytics
  • Chapter 10: Text Mining
  • Chapter 11: Social Media Analysis
  • Chapter 12: The Basics of Machine Learning
  • Publishing
  • Chapter 13: LaTex Basics

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