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http://dx.doi.org/10.3743/KOSIM.2015.32.1.153

Combining Ego-centric Network Analysis and Dynamic Citation Network Analysis to Topic Modeling for Characterizing Research Trends  

Yu, So-Young (한남대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.32, no.1, 2015 , pp. 153-169 More about this Journal
Abstract
The combined approach of using ego-centric network analysis and dynamic citation network analysis for refining the result of LDA-based topic modeling was suggested and examined in this study. Tow datasets were constructed by collecting Web of Science bibliographic records of White LED and topic modeling was performed by setting a different number of topics on each dataset. The multi-assigned top keywords of each topic were re-assigned to one specific topic by applying an ego-centric network analysis algorithm. It was found that the topical cohesion of the result of topic modeling with the number of topic corresponding to the lowest value of perplexity to the dataset extracted by SPLC network analysis was the strongest with the best values of internal clustering evaluation indices. Furthermore, it demonstrates the possibility of developing the suggested approach as a method of multi-faceted research trend detection.
Keywords
Ego-centric network; SPLC; Topic Modeling; DBI; Index S;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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