摘要
酒店在线评论的收集和分析可以反映顾客的满意度,利用得当,可以增加订单转化率,为酒店创造长期收益。杭州作为新兴国际城市,外国顾客日益增多,对于高级酒店的消费日趋增多。因此,分析外国顾客的满意度组成,对于杭州高级酒店日后的管理有着借鉴意义。与传统的内容分析相比,词频分析具有批量处理大量数据、文本的优势。基于此,本文采用AntConc软件进行词频分析,得出主要结论:(1)房间、服务、位置、食物和价格是提及最多的评论属性词;(2)外国顾客不是一味追求性价比,他们更加在乎房间的舒适度、干净度,以及服务人员的英文流畅度;(3)维护泳池、酒吧等酒店设施,并打造自身特色服务,可以吸引外国顾客。在此基础上,对酒店管理者提出几点建议,旨在提升酒店的国际竞争力。
The collection and analysis of hotel online comments can reflect customer satisfaction.If used properly,it can increase the order conversion rate to create long-term benefits for the hotel.As an emerging international city,Hangzhou are increasingly attracting more and more foreign customers,thus bringing about a remarkable consumption of high-end hotels with each passing day.Therefore,the analysis of the foreign customer satisfaction can be a useful reference for the managers of high-end hotels in Hangzhou.Compared with traditional content analysis,word frequency analysis has the advantage of batch processing a large amount of data and text.Based on this,this paper draws the main conclusions by adopting AntConc software:1)Such elements as the room,service,location,food and price are the most mentioned comment attribute words;2)Foreign customers are more concerned about the comfort and cleanliness of the room and the fluency of the service staff’s English,instead of blindly pursuing low prices;3)Other hotel facilities such as clean swimming pools and bars with their special characteristics can attract foreign customers.On the basis of this,this paper puts forward several suggestions to hotel managers to enhance international competitiveness.
作者
方旻圆
饶华清
FANG Minyuan;RAO Huaqing(School of Tourism and Foreign Languages,Tourism College of Zhejiang,Hangzhou 311231,China;School of Tourism Services and Management,Tourism College of Zhejiang,Hangzhou 311231,China)
出处
《浙江海洋大学学报(人文科学版)》
2023年第3期75-82,共8页
Journal of Zhejiang Ocean University(Humanities Sciences)
基金
浙江旅游职业学院院级重点课题(编号:2129KY04087)。
关键词
星级酒店
在线评论
词频分析
顾客满意度
文本挖掘
英文评论
star hotels
online comments
word frequency analysis
customer satisfaction
text mining
English comments