基于文本挖掘的电动汽车分时租赁项目服务质量改进

Service Quality Improvement of Electric Vehicle Timeshare Rental Project Based on Text Mining Technology

摘要:

针对大数据时代电动汽车企业分时租赁项目服务质量改进问题，基于用户网络评论数据，将文本挖掘技术和质量功能展开相结合，进行顾客需求识别和电动汽车分时租赁企业服务质量改进研究，并以上海典型企业EVCARD为例进行验证。首先对爬取的顾客评论数据进行分词和词性标注处理，然后运用正则表达式提取特征情感词对并计算其词频和综合情感值，从中识别出8项需改进的顾客需求项；基于此，应用质量功能展开工具，在质量屋左墙建设中，制定了关注度与综合情感值相结合的顾客需求权重确定方法；最后通过文献研究和专家访谈，识别出17个服务质量要素并对其进行排序和分级，从定制个性化服务体系等角度为企业提出了相应的服务质量改进建议。

关键词:电动汽车;分时租赁;文本分析;情感分析;质量功能展开

Abstract:

To address the issue of service quality improvement in electric vehicle （EV） timeshare rental projects in the era of big data， this paper， by combining text mining techniques with quality function deployment （QFD）， identifies customer demands and improves the service quality of EV rental enterprises， taking EVCARD， a typical Shanghai-based company， as a case study. First， customer review data were collected， segmented， and tagged for parts of speech. Using regular expressions， feature-emotion word pairs were extracted， their word frequency and comprehensive sentiment values were calculated， and eight customer demands requiring improvement were identified. Based on these findings， the QFD tool was applied， and a method combining customer demand attention with comprehensive sentiment values was used to determine customer demand weights in the construction of the left wall of the quality house. Subsequently， through literature research and expert interviews， 17 service quality elements were identified， ranked， and classified. Finally. recommendations for service quality improvement， such as customized service systems， were proposed for enterprises.

Key words:electric vehicle (EV);EV timeshare rent;textual analysis;emotion analysis;quality function deployment (QFD)