Purpose

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

Design/methodology/approach

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Findings

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

Research limitations/implications

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

Practical implications

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

Originality/value

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

中文翻译：

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燃油车还是新能源车？基于网络评论的汽车消费者需求差异化研究

目的

作者希望从网络评论中筛选出有价值的信息，客观准确地了解汽车消费者的需求信息，帮助汽车企业制定更合理的生产和营销策略，实现健康可持续发展。本文旨在讨论上述目标。

设计/方法论/途径

作者从在线汽车论坛收集了评论数据，并经过预处理生成了语料库。然后，作者使用LDA模型提取消费者需求和主题。最后，作者使用经过训练的Word2vec工具来扩展消费者需求主题。

发现

不同类型的汽车消费者有相同的需求，如“空间”、“动力性能”、“品牌对比”，也有不同的需求，如“外观”、“安全”、“服务”、“新能源功能” ”; 消费者购买新能源汽车仍习惯于与燃油车品牌或型号进行比较；新能源汽车消费者在购买和使用过程中更加注重服务和服务质量。

研究局限性/影响

新能源汽车的研发时间较短，有些车型的上市时间只有一年甚至六个月。可用数据量较少可能会影响主题模型的适用性。需要增加样本量，特别是新能源汽车的样本量，进一步提高主题模型的普遍适用性。

实际影响

一是有利于在线评论网站完善现有评论发布机制，提升在线评论内容整体质量，增加用户流量，促进在线评论网站健康发展。其次，可以及时调整未来的产品生产和销售计划，进一步增强汽车企业利用在线评论进行网络营销的能力。

原创性/价值

作者提高了融合主题模型的准确性和稳定性，为在线评论的多维主题挖掘提供了科学高效的研究工具。借助研究结果，消费者可以更轻松地理解讨论主题，从而过滤出有价值的参考信息。因此，汽车公司可以获得有关消费者需求和产品质量反馈的信息，从而快速调整生产和营销策略，以增加销量和市场份额。