Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives. Gabe Ignatow and Rada F. Mihalcea′s new text An Introduction to Text Mining will be a starting point for undergraduates and first-year graduate students interested in collecting and analyzing textual data from online sources, and will cover the most critical issues that students must take into consideration at all stages of their research projects, including: ethical and philosophical issues; issues related to research design; web scraping and crawling; strategic data selection; data sampling; use of specific text analysis methods; and report writing.
Author(s): Gabe Ignatow, Rada Mihalcea
Publisher: SAGE
Year: 2018
Language: English
Pages: 0
Tags: Data Science, Text Mining
Chapter 1. Text Mining and Text Analysis
Chapter 2. Acquiring Data
Chapter 3. Research Ethics
Chapter 4. The Philosophy and Logic of Text Mining
Chapter 5. Designing Your Research Project
Chapter 6. Web Scraping and Crawling
Chapter 7. Lexical Resources
Chapter 8. Basic Text Processing
Chapter 9. Supervised Learning
Chapter 10. Analyzing Narratives
Chapter 11. Analyzing Themes
Chapter 12. Analyzing Metaphors
Chapter 13. Text Classification
Chapter 14. Opinion Mining
Chapter 15. Information Extraction
Chapter 16. Analyzing Topics
Chapter 17. Writing and Reporting Your Research
Appendix A. Data Sources for Text Mining
Appendix B. Text Preparation and Cleaning Software
Appendix C. General Text Analysis Software
Appendix D. Qualitative Data Analysis Software
Appendix E. Opinion Mining Software
Appendix F. Concordance and Keyword Frequency Software
Appendix G. Visualization Software
Appendix H. List of Websites
Appendix I. Statistical Tools
Glossary
References
Index