Two layer-based trajectory analysis of the research trend in automotive fuel industry

Na Kyeong Lee, Yukyeong Han, Wei Xong, Min Song

Research output: Contribution to journalArticle

Abstract

The increasing concern of climate change and unstable oil prices induce the development of technological fuel in automobile industry. To investigate such a rapidly changing path, researchers apply bibliometrics and topic modeling to patent data. These commonly used methods, however, have several drawbacks such as considering macro-level trend only and focusing on high probable terms. To avoid these weaknesses, we propose the two-layer trend analysis based on Time country topic model (TCT) and Dirichlet compound multinomial model (DCM) that enable to detect both macro-level and micro-level trend and identify bursty terms in automotive industry. Experimental results show rising, falling and fluctuating trend topics on condition of countries using TCT model. We also find path of automotive technology based on bursty terms from the analysis of DCM model. Specifically, electric vehicle, aluminum in lightweight material and diesel engine are considered as rising topics in the automobile fuel. Our proposed framework can be applied to analyze the trajectory analysis in various other fields.

Original languageEnglish
Pages (from-to)1701-1719
Number of pages19
JournalScientometrics
Volume124
Issue number3
DOIs
Publication statusPublished - 2020 Sep 1

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

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