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[目的 /意义]大数据环境下,文本挖掘和情感分析技术在产品、服务等网络点评分析中得到越来越广泛的应用。通过对大规模文本数据情感挖掘,研究影响企业舆情的关键要素。[方法 /过程]基于中国大陆292个城市103 878家酒店的2 500多万条网络点评数据,挖掘企业在线舆情,识别影响顾客服务体验的关键内容要素。采用探索性因子分析方法对关键要素进行归类,并通过多元回归分析得出评论内容要素与顾客总体满意度之间的关系。[结果 /结论 ]酒店客房要素和电器要素对酒店业顾客总体满意度影响最大。本研究方法和结论为服务企业营销和管理的大数据商业分析研究提供参考。
[Purpose / Significance] In the context of big data, text mining and sentiment analysis techniques are gaining more and more widespread use in the online reviews of products, services and the like. Through the excavation of large-scale text data emotion, research the key elements that affect the public opinion of the enterprise. [Method / Process] Based on over 25 million online review data from 103,878 hotels in 292 Chinese cities, tap into the online public opinion and identify key content elements that impact the customer service experience. Exploratory factor analysis method is used to classify the key elements, and through multivariate regression analysis to find the relationship between the content of the comments and the overall customer satisfaction. [Result / Conclusion] Hotel room elements and electrical elements have the greatest impact on the hotel industry overall customer satisfaction. The research methods and conclusions provide reference for big data business analysis and research on service marketing and management.