Topic based research competitiveness evaluation

Research competitiveness analysis refers to the measurement, comparison and analysis of the research status (i.e., strength and/or weakness) of different scientific research bodies (e.g., institutions, researchers, etc.) in different research fields. Improving research competitiveness analysis method can be conducive to accurately obtaining the research status of research fields and research bodies. This paper presents a method of evaluating the competitiveness of research institutions based on research topic distribution. The method uses the LDA topic model to obtain a paper-topic distribution matrix to objectively assign the academic impact of papers (such as number of citations) to research topics. Then the method calculates the competitiveness of each research institution on each research topic with the help of an institution-paper matrix. Finally, the competitiveness and the research strength and/or weakness of the institutions are defined and characterized. A case study shows that the method can lead to an objective and effective evaluation of the research competitiveness of research institutions in a given research field.

[1]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[2]  H. Small,et al.  Identifying emerging topics in science and technology , 2014 .

[3]  Tânia F. G. G. Cova,et al.  Iberian universities: a characterisation from ESI rankings , 2012, Scientometrics.

[4]  Yang Zhang,et al.  Combining paper cooperative network and topic model for expert topic analysis and extraction , 2017, Neurocomputing.

[5]  Zuo Wen-ge.  An ESI-based Analysis of the Competitiveness in Plant & Animal Sciences of China Agricultural University , 2012 .

[6]  Yoshiyuki Takeda,et al.  Detecting emerging research fronts based on topological measures in citation networks of scientific publications , 2008 .

[7]  Henry G. Small,et al.  Citation structure of an emerging research area on the verge of application , 2009, Scientometrics.

[8]  O. Faust.  Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017 , 2018 .

[9]  Gary G. Yen,et al.  Time line visualization of research fronts , 2003, J. Assoc. Inf. Sci. Technol..

[10]  Alan L. Porter,et al.  Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research , 2016 .

[11]  Arho Suominen,et al.  Modeling : Comparison of Unsupervised Learning and Human-Assigned Subject Classification , 2015 .

[12]  Chaomei Chen,et al.  Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace , 2012, Expert opinion on biological therapy.

[13]  Gideon S. Mann,et al.  Bibliometric impact measures leveraging topic analysis , 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).

[14]  Chaomei Chen,et al.  CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature , 2006, J. Assoc. Inf. Sci. Technol..

[15]  Alan L. Porter,et al.  Clustering scientific documents with topic modeling , 2014, Scientometrics.

[16]  Yu. V. Mokhnacheva,et al.  Research performance of RAS institutions and Russian universities: A comparative bibliometric analysis , 2011 .

[17]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[18]  Dong Zhenge,et al.  Investigation into Library Service Model of University Discipline Evaluation on the Basis of ESI and InCites Databases , 2014 .