Earticle

초록

영어

As the new energy vehicle (NEV) industry accelerates its transformation from electrification toward intelligence, intelligent driving functions have become one of the key factors influencing user satisfaction and brand competitiveness. Traditional satisfaction surveys rely heavily on structured questionnaires and struggle to uncover the complex latent topics and emotions embedded in massive volumes of unstructured online reviews. This study therefore applies the BERTopic topic-modeling method to systematically mine the core concerns, sentiment tendencies, and topic-evolution patterns of NEV intelligent-driving users, and constructs an integrated “topic–sentiment–satisfaction” analytical framework that provides data-driven decision support for automakers. Review texts were collected from four platforms—Dongchedi, Autohome, Weibo, and Zhihu—yielding 5,071 valid comments after cleaning, word segmentation, and SnowNLP-based sentiment annotation. BERTopic was then used for topic modeling, combined with sentiment analysis, brand comparison, and topic-evolution analysis. The results show that negative reviews account for 55.0% while positive reviews account for only 1.8%, indicating a strong overall tendency toward negative expression; 62 topics were identified, of which “overall vehicle experience” was dominant at 47.9%. Intelligent-driving topics such as “Tesla FSD,” “AITO M7 pricing,” and “NIO ET5T & VLA” were highly active, with negativesentiment proportions generally exceeding 60%. Brand comparison revealed differentiated discussion foci around each brand’s signature technologies, and topic-evolution analysis found explosive growth of intelligent-driving topics from 2024 onward, closely tracking technology iterations and product launches. Theoretically, the study extends BERTopic to the vertical NEV intelligent-driving domain and verifies its effectiveness on short, term-dense texts; practically, it reveals widespread user dissatisfaction with the maturity of intelligent-driving functions and offers automakers actionable recommendations.

목차

Abstract
1. INTRODUCTION
1.1. Research Background
1.2. Literature Review
1.3. Research Objectives and Methods
2. DATA COLLECTION AND PREPROCESSING
2.1. Data Sources and Collection
2.2. Data Preprocessing
2.3. Word-Frequency Analysis and Visualization
3. MODEL CONSTRUCTION AND ANALYSIS
3.1. Sentiment Analysis Based on SnowNLP
3.2. Topic Modeling Based on BERTopic
4. RESULTS AND DISCUSSION
4.1. Sentiment Differences Across Topics
4.2. Brand Sentiment Differences
4.3. Topic Evolution Over Time
4.4. Strategy Recommendations
5. CONCLUSION
REFERENCES

키워드

저자

• Keyan Li [ School of Digital Economy, Hubei University of Automotive Technology, Shiyan, China ]
• Pengfei Qin [ School of Management, Suzhou University, Suzhou, Anhui, China ] Corresponding Author
• Jingyuan Chen [ School of Digital Economy, Hubei University of Automotive Technology, Shiyan, Hubei, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of Digital Intelligence and Management Systems

• 간기

  연3회

• eISSN

  3140-5851

• 수록기간

  2026~2026

• 십진분류

  KDC 569 DDC 620