Deductive Text Analytics: Using Categorization Models for Confirmatory Approaches and Hypothesis Testing
Host: Derrick Cogburn
Date and Time: Thursday September 24, 2020, 12-1pm ET
In this session, Dr. Cogburn will introduce participants to categorization models, a powerful deductive technique for text mining. Researchers build categorization models (also known as dictionaries or lexicons), to represent a specific concept or construct of interest. These approaches help to identify the extent to which a specific concept is present or absent within a specific large-scale textual dataset. These categorization models can be relatively simple bifurcations of concepts, or require multiple levels and sub-levels supported by specific words, phrases and/or rules to help identify the specific content of interest. There are many intriguing applications of this technique, one of the more popular being sentiment analysis, where text may be categorized as containing “positive” or “negative” sentiment. Dr. Cogburn will discuss how to either identify and use existing categorization models, or to build, validate and deploy your own models to answer specific research questions or test hypotheses.
About Derrick Cogburn:
- Derrick has ten plus years working with QDA Miner and WordStat.
- Professor of Information Technology & Analytics, Kogod School of Business, American University, Professor of International Communication and International Development, School of International Service, American University
- Teaches graduate seminars on Big Data Analytics and Text Mining (using ProSuite and R/Rstudio)
- Coordinates multiple United Nations workshops/side events on text mining in international affairs, sustainable development goals (SDGs), and CRPD Implementation.
- Derrick has published multiple articles and conference papers on text mining
Click on the following link to Register