Identification of Trends in Automotive Industries Based on Text Mining

Hafizah Farhah Saipan Saipol; Syarifah Zyurina Nordin; Amir Syafiq Syamin Shah Amir Hamzah; Naoki Ohshima

doi:10.37934/araset.60.4.157170

Authors

Hafizah Farhah Saipan Saipol Department of Management of Technology (MoT), Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
Syarifah Zyurina Nordin Department of Management of Technology (MoT), Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
Amir Syafiq Syamin Shah Amir Hamzah Department of Management of Technology (MoT), Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
Naoki Ohshima Graduate School of Management of Innovation and Technology, Yamaguchi University, Yamaguchi, 753-0841, Japan

DOI:

https://doi.org/10.37934/araset.60.4.157170

Keywords:

Patent analysis, Text mining, Automotive industry, Natural language processing

Abstract

Understanding the trends of evolution in a technology is essential to make appropriate decisions. This paper aimed at mapping the technological evolution in automotive industries and understanding the main trends through patent data. Thus, the objective of this paper is to provide a structured overview for identifying the trend of patent in three automotive companies by employing text mining techniques. In natural language processing (NLP), text mining is used to determine several features such as segmentations and attributes of data. To achieve the objective, the abstract of patent documents in the period of the last two decades are mined from the DWPI database. To demonstrate the behaviour of selected patent documents, word cloud, co-occurrence networks and correspondence analysis are presented.