Industry 4.0: Latent Dirichlet Allocation and clustering based theme identification of bibliography

Abstract Industry 4.0 with its fusion of technologies has brought an unprecedented growth in all sectors. In this paper, we first present the several pillars of Industry 4.0 which have contributed to the advancement of this field. Then, we perform a bibliometric profile of the Industry 4.0 publications in Web of Science by analyzing the publications and citation structure, most referenced publications, most productive and influential authors, organizations and countries. Further, an extensive analysis of the key words is presented along with the citation bursts of top keywords. Various visualizations tools have been to used show relationships between different bibliometric profiles. The individual growth of different research areas over the year is also presented. The results show promising growth of Industry 4.0 as a research area, various collaborative groups have been identified which are working extensively in the growth of the domain, spearheaded by China, however, considerable contribution of developing countries is also there. An abstract analysis is done using K-means clustering and Latent Dirichlet Allocation (LDA) to identify key research themes.