Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1006315 | Journal of Engineering and Technology Management | 2014 | 13 Pages |
Abstract
We are developing indicators for the emergence of science and technology (S&T) topics. To do so, we extract information from various S&T information resources. This paper compares alternative ways of consolidating messy sets of key terms [e.g., using Natural Language Processing on abstracts and titles, together with various keyword sets]. Our process includes combinations of stopword removal, fuzzy term matching, association rules, and term commonality weighting. We compare topic modeling to Principal Components Analysis for a test set of 4104 abstract records on Dye-Sensitized Solar Cells. Results suggest potential to enhance understanding regarding technological topics to help track technological emergence.
Related Topics
Social Sciences and Humanities
Business, Management and Accounting
Accounting
Authors
Nils C. Newman, Alan L. Porter, David Newman, Cherie Courseault Trumbach, Stephanie D. Bolan,