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Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.
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Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.
Produktdetails
- Produktdetails
- Verlag: Sage Publications Ltd
- Seitenzahl: 360
- Erscheinungstermin: 14. Dezember 2024
- Englisch
- Abmessung: 244mm x 170mm x 19mm
- Gewicht: 640g
- ISBN-13: 9781529620047
- ISBN-10: 152962004X
- Artikelnr.: 70945319
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
- Verlag: Sage Publications Ltd
- Seitenzahl: 360
- Erscheinungstermin: 14. Dezember 2024
- Englisch
- Abmessung: 244mm x 170mm x 19mm
- Gewicht: 640g
- ISBN-13: 9781529620047
- ISBN-10: 152962004X
- Artikelnr.: 70945319
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
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.
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
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
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
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







