关键词: 在线评论, LDA主题模型, 概率语言术语集, Shapley值, 广义TODIM方法

Abstract: New energy vehicles have become one of the emerging trends in the development of the automobile industry by virtue of their clean and environmentally friendly advantages. Over the past decade, the Chinese government has implemented a series of financial policies and tax benefits to promote the development of China’s new energy vehicles industry. At the end of 2022, the government announced the termination of the subsidy policy for the purchase of new energy vehicles. The rapid industry development and policy changes have shifted the development of new energy vehicles from policy-driven to market-oriented. With a growing maturity of e-commerce and third-party website evaluation systems, consumers are faced with the extraneous task of extracting useful information from massive online reviews to make an educated product purchasing choice. In this paper, we introduce a novel model for evaluating and selecting new energy vehicles based on online consumer reviews within the context of the Internet big data environment. Based on the evaluation outcomes, the study provides recommendations and references for governmental bodies, enterprises, and consumers involved in the design, recommendation, and selection of new energy vehicles. This proactive approach aligns with the overarching objective of achieving a “dual-carbon” strategy, promoting sustainability, and facilitating the advancement of the new energy vehicles industry. In essence, this study plays a pivotal role in contributing to the realization of the “dual-carbon” goal while fostering the robust development of the new energy vehicle industry.
First, this paper establishes a comprehensive evaluation index system of new energy vehicles. Leveraging advanced data mining and text analysis techniques, this paper crawls, processes and restructures the online consumer review data about new energy vehicles from third-party websites. Using the Latent Dirichlet Allocation (LDA) theme model, this paper identifies consumer preference information, which is then used as the foundation for decision makers to identify evaluation attributes. This information, in conjunction with market analysis and new energy vehicles specific characteristics, informs the construction of a pertinent set of evaluation indices, thereby facilitating the establishment of a robust evaluation index framework. Next, we develop an evaluation model based on multi-attribute decision. Combining the sentiment lexicon analysis and probabilistic linguistic term set, the sentiment preferences in the textual comment information are fully converted to obtain the probabilistic linguistic evaluation matrix to avoid the loss of information. In cases where evaluation information is available, our paper takes into account information sharing and attribute correlation. We introduce the λ fuzzy measure, Shapley function value, and the weight value of the associated attributes to obtain the weight value. Considering the limited rational behavior in product evaluation choice, we further integrate the generalized TODIM method to construct a new ranking model to fully portray the loss aversion psychology of consumers.
This paper focuses on the selection of four new energy vehicles as the evaluation object and the constructed model is applied to evaluate the selection choice. Based on the sensitivity analysis, the evaluation ranking results are robust to changes in the degree of risk aversion in the present model. A comparative analysis further verifies that the results obtained by applying the present model are more accurate and effective than other existing models.
The study shows that in the data-driven product evaluation model, it is necessary to establish a scientific and reasonable evaluation index system. The transformation of the multiple emotional preference information found within textual comments into actionable insights is paramount. Additionally, our paper considers the information sharing among online comments and the correlation between evaluation, all while accommodating consumers’ loss aversion tendencies. The outcome achieved through this comprehensive approach aligns more closely with the genuine evaluation of new energy vehicles, carrying substantial theoretical and practical significance for the selection and assessment of these vehicles.

Key words: online reviews, LDA topic models, probabilistic linguistic term sets, Shapley values, generalized TODIM method