A Study of the Subject Categorization of the MIS-related Journals in the ISI Databases Using Topical Features in the Text Content and Machine Learning Methods

Abstract

In this study we analyzed and discussed that the MIS-related journals under the ISI subject category of IS&LS are simultaneously given with subject category Management, using methods of topic modeling, journal clustering and subject category prediction. In the experiment of journal clustering, all journals under subject category Management and other journals also having similar topical features can be gathered into a cluster, and “management” is their common and the most distinct topic. Because the journals belonged to this cluster are almost same to those in the MIS clusters generated by the previous studies, we considered it as the MIS cluster in this study. In the second experiment, we used the classification and regression tree (CART) technique to predict assignment of subject category with that the journals in the original subject category Management and in the MIS cluster produced in this study as positive examples, respectively. The trees generated by the two tests both used the occurring probabilities of the topic “management” as the main classification rule. However, in the latter test, we did not only obtain a simpler classification tree but also had a result with less predicting errors. This means that if all journals in the MIS cluster could be given with subject category Management, the retrieval results can be more effective and complete.

Keywords

• ISI subject category
• Machine learning
• Topic modeling;Journal clustering
• Category prediction