TY - JOUR
T1 - Analyzing unstructured text data
T2 - Using latent categorization to identify intellectual communities in information systems
AU - Larsen, Kai R.
AU - Monarchi, David E.
AU - Hovorka, Dirk S.
AU - Bailey, Christopher N.
PY - 2008/11
Y1 - 2008/11
N2 - The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.
AB - The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.
UR - http://www.scopus.com/inward/record.url?scp=53349151358&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2008.02.009
DO - 10.1016/j.dss.2008.02.009
M3 - Article
AN - SCOPUS:53349151358
SN - 0167-9236
VL - 45
SP - 884
EP - 896
JO - Decision Support Systems
JF - Decision Support Systems
IS - 4
ER -