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

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

Volume 10, Issue 1

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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
Vol. 10, Issue 1, Pages: 79-84(2026)
Published：30 January 2026
DOI：10.47297/wspdecWSP2515-797316.20261001
Accepted：

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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：

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

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