Document Type
Article
Publication Date
1-2015
Keywords
Bibliometric, Business Analytics, Concept Mining, Heuristic, Research Continuum, Text Analytics, Text Mining
Digital Object Identifier (DOI)
http://doi.org/10.4236/iim.2015.71002
Abstract
Classification of research articles is fundamental to analyze and understand research literature. Underlying concepts from both text analytics and concept mining form a foundation for the development of a quantitative heuristic methodology, the Scale of Theoretical and Applied Research (STAR), for classifying research. STAR demonstrates how concept mining may be used to classify research with respect to its theoretical and applied emphases. This research reports on evaluating the STAR heuristic classifier using the Business Analytics domain, by classifying 774 Business Analytics articles from 23 journals. The results indicate that STAR effectively evaluates overall article content of journals to be consistent with the expert opinion of journal editors with regard to the research type disposition of the respective journals.
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Intelligent Information Management, v. 7, no. 1, p. 7-21
Scholar Commons Citation
Walczak, Steven and Kellogg, Deborah L., "A Heuristic Text Analytic Approach for Classifying Research Articles" (2015). School of Information Faculty Publications. 168.
https://digitalcommons.usf.edu/si_facpub/168
