Introduction to Text Analytics

Informationen zum Autor 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. Klappentext Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science. Inhaltsverzeichnis 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

Auteur

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.

Texte du rabat

Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.

Contenu

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