Understanding the User experience of battery electric vehicles: a perspective based on big data text mining Techniques
Authors: Quan Gu, Shengqing Huang, Zhang Jie, Yue Cui, Ying Zhang
Abstract: Battery electric vehicle (BEV) is the core innovation of low-carbon travel transformation, but there are still few evaluation studies on user experience. To more accurately understand the relatively real user experience of BEV, this paper uses text mining and natural language processing based on the big data text of BEV user experience and proposes a method for collecting, drawing, and analyzing these user experiences. In this way, the user experience of the real scene can be restored to a certain extent. The content includes are following. Firstly, obtain user comments on the typical BEV Model 3 on the online review website through crawler software, and use natural language processing technology to pre-process the data. Secondly, based on the constructed stop word database, the texts in the text stop words are eliminated. Then, the number of common occurrences between two adjacent words is counted, and a co-occurrence matrix is generated. Finally, word frequency statistics, improved TFIDF keyword extraction is performed; and keyword word cloud, centrality analysis, multi-scale keyword analysis are visualized. Compared with the traditional research focusing on individual user experience, this research explores the possibility of a research method of user experience evaluation in the context of big data. This will provide a certain theoretical reference for the research of the user experience evaluation system, and help the product user experience team of related BEV to get closer to the truth to understand the user's experience scenarios, behaviors, and real feelings.
Keywords: user-centered evaluation approaches, user experience visualization, battery electric vehicle, text mining, natural language processing
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