Why use text mining/analysis in your research?
"Researchers from all corners of academia and the private and public sectors are collecting and analyzing textual data from online platforms, and they are using this data to answer questions about social attitudes, trends and fashions, consumer behavior, political deliberations, social interactions, interpersonal relationships, and many other social phenomena."
Text mining: A guidebook for the social sciences (2017) Gabe Ignatow and Raha Micalcea
There are three main reasons to embark on text mining and analysis for research:
- systematic review of literature - automatic review of a large body of content very quickly; keep up with research in your field
- discovery of new insights - discovering patterns and trends can allow researchers to make connections and draw conclusions about the content of a large body of text
- computational linguistics research - improving the analysis, understanding and generation of human language from text
Further reading below provides examples of these three key uses of text analysis.
- UQ Library Guide: Using NVivo in systematic reviewsHow you can use NVivo in completing systematic reviews.
- Text Analysis Starter Guide: What You Need to KnowIn this guide, learn more about what text analysis is, how to perform text analysis using AI tools, and why it’s more important than ever to automatically analyze your text in real time.
- O’Mara-Eves, A. (2015) Using text mining for study identification in systematic reviews: a systematic review of current approachesThis review aims to increase awareness of the potential use of text mining for screening in systematic reviews and promote further collaborative research between the computer science and systematic review communities.
- Clark, J. (2012) Text mining and scholarly publishingWhat is text mining? Why do people want to do text mining? How to do text mining?
- Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as DataAn introduction to the use of text as an input to economic research. Discusses the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications.
The Cambridge Handbook of English Corpus Linguistics by Publication Date: 2015The Cambridge Handbook of English Corpus Linguistics (CHECL) surveys the breadth of corpus-based linguistic research on English, including chapters on computational tools and methods, collocations, phraseology, grammatical variation, historical change, and the description of registers and dialects.
Text Analytics: Advances and Challenges by Publication Date: 2020Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing.
