Abstract
Under the goal of carbon peak and carbon neutrality, developing battery electric vehicles (BEVs) is an important way to reduce carbon emissions in the transportation sector. To popularize BEVs as soon as possible, it is necessary to study selection strategies for BEVs from the perspective of consumers. Therefore, the Latent Dirichlet Allocation (LDA) model based on fine-grained sentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternatives. Fine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data. The LDA model is suggested to divide topics and construct the indicator system. The MCDM model is used to rank vehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in China. The results show that (a) via the LDA model based on fine-grained sentiment analysis, attributes that consumers care most about are divided into five topics: dynamics, technology, safety, comfort, and cost; (b) based on the DEMATEL technique, the dimensions in the order of importance are as follows: safety, technology, dynamics, comfort, and cost; (c) the price is the most important criteria that affect customers’ satisfaction by the DANP model; and (d) based on the VIKOR model, the selection strategies present that Aion S is highlighted as the best choice, and the optimization path is discussed to promote the performance of BEVs to increase customers’ satisfaction. The findings can provide a reference for improving the sustainable development of the automobile industry in China. The proposed framework serves as the basis for further discussion of BEVs.
Highlights
With the rapid growth of the number of vehicles, the issue of energy consumption and greenhouse gas (GHG) emissions in the transportation sector are attracting increasing attention worldwide [1, 2]
Latent Dirichlet Allocation (LDA) is one of the topic models, which is applied to automatically discover topics in the text that consumers are most satisfied and least satisfied with. e core computational problem for topic models is to use the collected text to infer the hidden topic structure [66]. us, this study identifies the consumer concerns by using the LDA model, to discover key topics from the collected text
Building Evaluation Indicator System. e online wordof-mouth of battery electric vehicles (BEVs) is from the Autohome website, which is a relatively wellknown auto website in China
Summary
With the rapid growth of the number of vehicles, the issue of energy consumption and greenhouse gas (GHG) emissions in the transportation sector are attracting increasing attention worldwide [1, 2]. China owns the largest automobile market in the world since 2009 [10], and car ownership has exceeded 200 million by 2020, indicating that the issue of energy security and environmental pollution will become more prominent [11, 12]. To alleviate these problems, governments and automobile manufacturers pay more attention to develop cleaner and more efficient alternative-fuel vehicles [12, 13], which induces the upsurge of battery electric vehicles (BEVs) [14,15,16,17]. BEVs will become the mainstream of vehicle sales by 2035 in China [18]
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