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.
Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.
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
Dr. Emily Öhman is an Assistant Professor of Digital Humanities at Waseda University, Japan, where she bridges the gap between computational techniques and humanities research. Awarded her PhD in Language Technology from the University of Helsinki in 2021, she has since carved a niche for herself in the realms of sentiment analysis and emotion detection, particularly within narrative texts. Her work, which employs natural language processing (NLP) methods, spans a multitude of interdisciplinary projects, from computational literary studies to political science, and social media and communication studies analysis.
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