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.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (2019R1A2C2002577)
© 2020, Akadémiai Kiadó, Budapest, Hungary.
All Science Journal Classification (ASJC) codes
- Social Sciences(all)
- Computer Science Applications
- Library and Information Sciences