Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models

Runjia Hou; Haoyang Liu

doi:10.47297/wspdecWSP2515-797316.20261001

PDF
导出
分享
收藏
专辑

Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models

10卷第1期

浏览量: 0 Downloads: 0

Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models
Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models
School of Statistics and Data Science, Capital University of Economics and Business, Beijing, 210093, China
2026年10卷第1期页码：79-84
纸质出版：2026-01-30
DOI：10.47297/wspdecWSP2515-797316.20261001
Accepted：

Scan QR Code

Runjia Hou & Haoyang Liu. (2026). Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models. 发展经济学, 10(1), 79-84.

Hou Runjia,Liu Haoyang.Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models[J].Development Economics of China,2026,10(01):79-84.
Runjia Hou & Haoyang Liu. (2026). Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models. 发展经济学, 10(1), 79-84. DOI： 10.47297/wspdecWSP2515-797316.20261001.

Hou Runjia,Liu Haoyang.Research on New Energy Vehicle User Concern Analysis and Sentiment Evolution Based on Text Mining: A Case Study of Top 20 Best-selling Models[J].Development Economics of China,2026,10(01):79-84. DOI： 10.47297/wspdecWSP2515-797316.20261001.

摘要

Abstract

In the context of the rapid increase in the market penetration rate of new energy vehicles

pain points such as range anxiety restrict the development of the industry. Existing studies mostly treat the entire review as a single sentiment calculation unit

ignoring the multidimensionality and complexity of consumer reviews

which leads to insufficient accuracy. Therefore

this study conducts text mining based on 10

981 owner reviews of the top 20 best-selling models on the Dongchedi platform. By extracting core features through TF-IDF and co-word networks

the LDA model is utilized to identify five major themes: overall vehicle evaluation

driving experience

car purchase decision

household comfort

and range anxiety. Combining SnowNLP technology for fine-grained sentiment quantification effectively avoids the bias where local emotions are masked by overall evaluations. The study finds that user sentiments exhibit significant polarization: there is high satisfaction with cabin comfort and overall vehicle performance

but the proportion of negative sentiments in the range anxiety dimension is relatively high. The research results accurately reveal the current technical and service shortcomings of the industry

providing decision-making support for car companies' optimization.

关键词

Keywords

references

Blei D. M. , Ng A. Y. , & Jordan M . I . ( 2003 ). Latent dirichlet allocation. Journal of Machine Learning Research , 3(Jan), 993 – 1022 .

Guo Y. , Sun Q. , Su Y. , Guo Y. , & Wang C . ( 2021 ). Can driving condition prompt systems improve passenger comfort of intelligent vehicles? A driving simulator study . Transportation Research Part F: Traffic Psychology and Behaviour , 81 , 240 – 250 .

Hu F. , You Z. , & Cai J . ( 2025 ). An approach of sentiment-topic mining based on user online comments on new energy vehicles . International Journal of Computers Communications & Control , 20(6) .

Liu Y. , Zhang M. , Chen X. , Li K. , & Tang L . ( 2024 ). The impact of consumer sentiment on sales of new energy vehicles: Evidence from textual analysis . World Electric Vehicle Journal , 15 ( 7 ), 318 .

Mao R. , Xu W. , Qian Y. , Li X. , Li Y. , Li G. , & Zhang H . ( 2025 ). Understanding the determinants of electric vehicle range: A multi-dimensional survey . Sustainability , 17 ( 10 ), 4259 .

McCallum A . ( 1998 ). A comparison of event models for naive bayes text classification .

Paramita A. S. , & Jusak J . ( 2025 ). Fine-grained sentiment analysis approach on customer reviews based on aspect-level emotion detection . Journal of Applied Data Sciences , 6 ( 3 ), 2235 – 2247 .

Purwanto E. , & Irawan A . P . ( 2024 ). The willingness to purchase electric vehicles: A comprehensive review . In IOP Conference Series : Earth and Environmental Science (Vol . 1324, No. 1 , p. 012091 ). IOP Publishing.

Rathi R. N. , & Mustafi A . ( 2023 ). The importance of term weighting in semantic understanding of text: A review of techniques . Multimedia Tools and Applications , 82 ( 7 ), 9761 – 9783 .

Wu Z. , He Q. , Li J. , Bi G. , & Antwi-Afari M . F . ( 2023 ). Public attitudes and sentiments towards new energy vehicles in China: A text mining approach. Renewable and Sustainable Energy Reviews , 178 , 113242 .

文章被引用时，请邮件提醒。

Submit

Research on the Mechanism of Carbon Emission Constraints Affecting the Export Competitiveness of China's New Energy Vehicles under the "Dual Carbon" Goals

A Study on the Overseas Expansion Strategies of New Energy Vehicle Enterprises Under Tariff Policies: A Case Study of BYD Group

Exploration on the High-Quality Development Path of New Energy Vehicles in China

International Financial Market Analysis and Investment Strategy Formulation Based on Social Network Data

Related Author

Meng Yuan School of Economics and Law, Liaoning University of Science and Technology

Xiaoyan Li School of Economics and Law, Liaoning University of Science and Technology

Jinze Li School of Economics and Law, Liaoning University of Science and Technology

Qinlin Lao Hangzhou Yu'hang Second High School

Yanan Li School of Economics and Management, Weifang University of Science and Technology

Tao Xie School of Economics, Shen Yang University

Jie Huang Nanjing University of Finance and Economics

Related Institution

School of Economics and Law, Liaoning University of Science and Technology

Hangzhou Yu'hang Second High School

School of Economics and Management, Weifang University of Science and Technology

School of Economics, Shen Yang University

Nanjing University of Finance and Economics

⁰