Using Text Mining Techniques to Identify Research Trends

Combining text mining techniques and bibliometric analysis can help uncover hidden information in scientific publications and unseen patterns and trends in research fields. Text mining may help researchers gain a more comprehensive understanding of the knowledge of a certain field hidden in a large amount of scientific literature. Clustering can provide a more detailed structured/architecture overview of a certain field. Social network analysis (SNA) explores core themes and allows researchers to better understand the developmental gains of a certain field. How do you think SNA enables companies to understand your purchasing decisions? What are some text mining techniques companies might use to find connections for customer demographic characteristics? Using one of the free tools listed here, map your own interactions with friends and the mutual brands advertised to you. What similarities do you see?

Abstract

The research goal of this paper is to identify major academic branches and to detect research trends in design research using text mining techniques. In this paper, the information about scientific literature in design research isprocessed. A combination of clustering and bibliometric analysis led to shaping four academic branches and summarizing each academic branch. Then, research trends and the evolution for each academic branch are explored. We perform a two-dimensional text mining approach, including bibliometric and network analysis, in order to detect trends of major academic branches. Specifically, the bibliometric characterization aims to assess design research area outputs, while the network analysis intends to reveal research trends in each academic branch of design research and the evolution of core research themes.

Keywords: text mining; bibliometric analysis; trend analysis; design research

Source: Binling Nie and Shouqian Sun, https://www.mdpi.com/2076-3417/7/4/401/htm
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