Incorporating citation impact into analysis of research trends.

Autor/inn/en	Lee, Minchul; Song, Min
Titel	Incorporating citation impact into analysis of research trends.
Quelle	In: Scientometrics, (2020) 2, S.1191-1224 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-020-03508-3
Schlagwörter	Topic model; NLP; Text mining; DMR; Trend analysis; Publication analysis
Abstract	Abstract In the past decades, there have been a number of proposals to apply topic modeling to research trend analysis. However, most of previous studies have relied primarily on document publication year and have not incorporated the impact of articles into trend analysis. Unlike previous trend analysis using topic modeling, we incorporate citation count, which can be viewed as the impact of articles, into trend analysis to shed a new light on the understanding of research trends. To this end, we propose the Generalized Dirichlet multinomial regression (g-DMR) topic model, which improves the DMR topic model by replacing a linear inner product in topic priors, $$\mathrm{exp}\left({{\varvec{x}}}_{d}\cdot {{\varvec{\lambda}}}_{t}\right),$$ with a more general form based on topic distribution function (TDF), $$\mathrm{exp}\left(\mathrm{f}\left({{\varvec{x}}}_{d}\right)\right)+\varepsilon$$. We use multidimensional Legendre Polynomial as TDF to capture publication year and the number of citations per publication simultaneously. In DMR model, since metadata could affect the document-topic distribution only monotonically and continuous values such as publication year and citation count need to be discretized, it is difficult to view the dynamic change of each topic. But the g-DMR model can handle various orthogonal continuous variables with arbitrary order of polynomial, so it can show more dynamic topic trends. Two major experiments show that the proposed model is better suited for topic generation with consideration of citation impact than DMR does for the trend analysis in the field of Library and Information Science in general and Text Mining in particular.
Erfasst von	OLC
Update	2023/2/05