Identification and Analysis of Doubt Directions in Weibo Comment Sections Regarding the Collision Experiment Between Li Auto i8 and CHENGLONG Based on LDA and BERT

Shucen Guo

doi:10.54254/2755-2721/2025.LD28789

Applied and Computational EngineeringOpen access

Identification and Analysis of Doubt Directions in Weibo Comment Sections Regarding the Collision Experiment Between Li Auto i8 and CHENGLONG Based on LDA and BERT

Research Article

Open Access

Identification and Analysis of Doubt Directions in Weibo Comment Sections Regarding the Collision Experiment Between Li Auto i8 and CHENGLONG Based on LDA and BERT

Shucen Guo ^1*

¹ Dundee International Institute, Central South University, Changsha, China

^*Corresponding author: 7803230109@csu.edu.cn

Published on 28 October 2025

ACE Vol.202

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-497-7

ISBN (Online): 978-1-80590-498-4

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Abstract

With the rapid development of the automotive industry and the growing influence of social media, public opinion on automotive safety tests has become a key factor affecting consumer trust and the reputation of corporations. In 2025, the collision experiment between Li Auto i8 and CHENGLONG truck caused controversy on Weibo, which authenticity of the test results and the safety performance of the vehicles were questioned by public. However, traditional public opinion analysis methods struggle to accurately capture both thematic focuses and emotional tendencies in large-scale unstructured comment data. Against this backdrop, this study focused on analyzing public opinion regarding the controversial collision experiment between Li Auto i8 and CHENGLONG truck, which happened in 2025. This research used a fusion method of LDA, BERT and data augmentation. It explored public opinion themes and the effectiveness of the model in automotive public opinion text analysis, which provided insights for car companies' responses. Data was selected from 599 Weibo comments, after cleaning, tokenizing, stopword filtering and augmenting, data reached 2803 samples. The LDA-BERT (Latent Dirichlet's Assignment - Bidirectional Encoder Representation) fusion model extracted four core topics, which achieved 98.93% sentiment classification accuracy. Findings showed the model effectively managed automotive public opinion. It helped car companies find the direction of public questioning. Limitations included insufficient negative emotion extraction in LDA and errors in recognizing niche slang. In the future, more research can be done on optimizing the subject title filtering logic. Although the research had limitations, it can still contribute methodologically and practically to automotive public opinion analysis.

Keywords:

Li Auto i8, Public opinion, LDA-BERT

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Cite this article

Guo,S. (2025). Identification and Analysis of Doubt Directions in Weibo Comment Sections Regarding the Collision Experiment Between Li Auto i8 and CHENGLONG Based on LDA and BERT. Applied and Computational Engineering,202,23-30.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.202

ISSN: 2755-2721(Print) / 2755-273X(Online)

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