An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

Hanyang Luo, Wugang Song, Wanhua Zhou (), Xudong Lin and Sumin Yu
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Hanyang Luo: Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China
Wugang Song: College of Management, Shenzhen University, Shenzhen 518060, China
Wanhua Zhou: College of Management, Shenzhen University, Shenzhen 518060, China
Xudong Lin: Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China
Sumin Yu: Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China

Sustainability, 2023, vol. 15, issue 18, 1-29

Abstract: This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management.

Keywords: Chinese automobile market; user preferences; online user-generated content; sentiment analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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