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Topic Mining and Sentiment Analysis for Consumer Reviews of Automotive Spare Parts on E-commerce Platforms
Research Article | Published: 09 July 2025
ICCK Transactions on Intelligent Systematics Volume 2, Issue 3, 2025: 137-148
Volume 2, Issue 3, ICCK Transactions on Intelligent Systematics
Volume 2, Issue 3, 2025
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Jianlei Kong
Jianlei Kong
Beijing Technology and Business University, China
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ICCK Transactions on Intelligent Systematics, Volume 2, Issue 3, 2025: 137-148

Research Article | 09 July 2025
Topic Mining and Sentiment Analysis for Consumer Reviews of Automotive Spare Parts on E-commerce Platforms
by
Minling Zeng Minling Zeng 1 *
Xiaoli Deng Xiaoli Deng 2
1 School of Management, Xiamen University Tan Kah Kee College, Zhangzhou 363105, China
2 School of Digital Economy and Management, Software Engineering Institute of Guangzhou, Guangzhou 510990, China
* Corresponding Author: Minling Zeng, [email protected]
DOI: 10.62762/TIS.2025.106283
Received: 22 April 2025, Accepted: 01 June 2025, Published: 09 July 2025  
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Abstract
This paper explores factors influencing consumer satisfaction in automotive spare parts e-commerce through text mining and sentiment analysis of Taobao reviews. By applying TF-IDF (Term Frequency-Inverse Document Frequency), semantic network analysis, and LDA (Latent Dirichlet Allocation) topic modeling, four core themes are identified: Logistics, Quality, Price, and Customer Service. A domain-specific sentiment lexicon constructed via the SO-PMI method reveals that positive reviews predominantly emphasize product reliability and logistics efficiency, while negative feedback focuses on installation complexity and inconsistent specifications. Based on these findings, targeted recommendations are proposed, including strengthening after-sales service management, enhancing product applicability, optimizing product designs for easier installation, and conducting market research for rational pricing. This research provides a methodological framework for enhancing consumer satisfaction in automotive e-commerce, aligning theoretical insights with practical operational needs.
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Topic Mining and Sentiment Analysis for Consumer Reviews of Automotive Spare Parts on E-commerce Platforms
Keywords
online reviews
topic mining
text classification
sentiment analysis
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Alalawin, A., Arabiyat, L. M., Alalaween, W., Qamar, A., & Mukattash, A. (2021). Forecasting vehicle's spare parts price and demand. Journal of quality in maintenance engineering, 27(3), 483-499.
    [CrossRef]   [Google Scholar]
  2. Celuch, K. (2021). Customers' experience of purchasing event tickets: mining online reviews based on topic modeling and sentiment analysis. International Journal of Event and Festival Management, 12(1), 36-50.
    [CrossRef]   [Google Scholar]
  3. Churchill, R., & Singh, L. (2022). The evolution of topic modeling. ACM Computing Surveys, 54(10s), 1-35.
    [CrossRef]   [Google Scholar]
  4. Dhaoui, C., Webster, C. M., & Tan, L. P. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480-488.
    [CrossRef]   [Google Scholar]
  5. Garner, B., & Kim, D. (2022). Analyzing user-generated content to improve customer satisfaction at local wine tourism destinations: an analysis of Yelp and TripAdvisor reviews. Consumer behavior in tourism and hospitality, 17(4), 413-435.
    [CrossRef]   [Google Scholar]
  6. Gajewska, T., Zimon, D., Kaczor, G., & Madzík, P. (2020). The impact of the level of customer satisfaction on the quality of e-commerce services. International Journal of Productivity and Performance Management, 69(4), 666-684.
    [CrossRef]   [Google Scholar]
  7. Havrlant, L., & Kreinovich, V. (2017). A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation). International Journal of General Systems, 46(1), 27-36.
    [CrossRef]   [Google Scholar]
  8. Haralayya, B. (2021). Consumer Buying Behavior With Reference to Bajaj Auto Ltd. Iconic Research And Engineering Journals, 5(1), 131-140.
    [Google Scholar]
  9. Hsu, C. C., Chen, S. H., & Feng, X. C. (2024). Analysis of Product Quality and Customer Satisfaction: A Case Study of the Automotive Parts Industry. International Journal of Financial, Accounting, and Management, 6(2), 245-259.
    [CrossRef]   [Google Scholar]
  10. Hu, N. (2022). Sentiment analysis of texts on public health emergencies based on social media data mining. Computational and mathematical methods in medicine, 2022(1), 3964473.
    [CrossRef]   [Google Scholar]
  11. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia tools and applications, 78, 15169-15211.
    [CrossRef]   [Google Scholar]
  12. Jeong, B., Yoon, J., & Lee, J. M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290.
    [CrossRef]   [Google Scholar]
  13. Pavlinek, M., & Podgorelec, V. (2017). Text classification method based on self-training and LDA topic models. Expert Systems with Applications, 80, 83-93.
    [CrossRef]   [Google Scholar]
  14. Qi, L., Xie, Y., Zhang, Q., Zhang, J., & Ma, Y. (2025). Multi-feature fusion-based consumer perceived risk prediction and its interpretability study. PloS one, 20(1), e0316277.
    [CrossRef]   [Google Scholar]
  15. Qazi, A., Tamjidyamcholo, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). Assessing consumers' satisfaction and expectations through online opinions: Expectation and disconfirmation approach. Computers in Human Behavior, 75, 450-460.
    [CrossRef]   [Google Scholar]
  16. Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
    [CrossRef]   [Google Scholar]
  17. Sánchez, J., Callarisa, L., Rodríguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism management, 27(3), 394-409.
    [CrossRef]   [Google Scholar]
  18. Sheth, A., Arpinar, I. B., & Kashyap, V. (2004). Relationships at the heart of semantic web: Modeling, discovering, and exploiting complex semantic relationships. Enhancing the Power of the Internet, 63-94.
    [CrossRef]   [Google Scholar]
  19. Stevens, J. L., Spaid, B. I., Breazeale, M., & Jones, C. L. E. (2018). Timeliness, transparency, and trust: A framework for managing online customer complaints. Business Horizons, 61(3), 375-384.
    [CrossRef]   [Google Scholar]
  20. Xiao, L., Li, Q., Ma, Q., Shen, J., Yang, Y., & Li, D. (2024). Text classification algorithm of tourist attractions subcategories with modified TF-IDF and Word2Vec. PloS one, 19(10), e0305095.
    [CrossRef]   [Google Scholar]
  21. Ye, T., Zhao, S., Lau, C. K. M., & Chau, F. (2024). Social media sentiment of hydrogen fuel cell vehicles in China: Evidence from artificial intelligence algorithms. Energy Economics, 133, 107564.
    [CrossRef]   [Google Scholar]
  22. Zhang, S., Zhou, C., & Liu, Y. (2020). Consumer Purchasing Intentions and Marketing Segmentation of Remanufactured New‐Energy Auto Parts in China. Mathematical Problems in Engineering, 2020(1), 5647383.
    [CrossRef]   [Google Scholar]
  23. Zhao, Y., Xu, X., & Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International journal of hospitality management, 76, 111-121.
    [CrossRef]   [Google Scholar]
  24. Wu, Z., Duan, C., Cui, Y., & Qin, R. (2023). Consumers' attitudes toward low-carbon consumption based on a computational model: Evidence from China. Technological Forecasting and Social Change, 186, 122119.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Zeng, M., & Deng, X. (2025). Topic Mining and Sentiment Analysis for Consumer Reviews of Automotive Spare Parts on E-commerce Platforms. ICCK Transactions on Intelligent Systematics, 2(3), 137–148. https://doi.org/10.62762/TIS.2025.106283
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ICCK Transactions on Intelligent Systematics

ICCK Transactions on Intelligent Systematics

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