Research On Elements Of Shared Accommodation Experience Based On Text Analysis

The homestay industry has become a new outlet for the development of the sharing economy,and shared accommodation has increasingly become a choice for people to travel.The Internet platform has created a good communication platform for tourists and businesses.Tourists can purchase products quickly and easily.The comments published through the OTA platform also provide businesses with many valuable information resources.In order to better meet the personalized accommodation needs of tourists,the textual data of the reviews of tourists has great research significance.The study is based on the travel experience theory,using data mining,content analysis,cluster analysis,and literature research methods.The content of Airbnb's tourist review text is divided into research objects,and the comprehensive use of travel experience theory and cluster analysis on the travel experience of tourists Carry out analysis and extract relevant elements that affect the tourist experience,based on this,try to put forward feasibility suggestions to improve the tourist experience.First of all,the domestic and foreign studies on sharing economy and tourism experience are systematically sorted out,and the current research focuses and deficiencies are summarized.Combined with data mining technology and Python programming language,tourism experience theory is mainly used to provide theoretical support for the research.On the basis of literature review,the connotation of sharing economy,data mining and cluster analysis is deeply understood,and the follow-up research is carried out on this basis.Secondly,it analyzes the general situation of the development of domestic homestay and the industry characteristics and existing problems of the development of Shared accommodation,and discusses the development of Airbnb Shared accommodation platform and various experience activities provided by it.Released based on the state information center,Shared accommodation properties of the top 10 cities Beijing,Shanghai,Xiamen,Guangzhou,Chengdu,Chongqing,Xian,Shenzhen,Hangzhou,Qingdao as the research object,through the Python language grab an platform of the ten cities on the visitors comment text information,using Jieba Chinese word segmentation tools for processing and analyzing the text content,on the basis of tourists experience elements extraction,using the K-Means clustering algorithm will related elements gathered in the same group,and then to experience factor concept model build,found surrounding environment experience is the foundation;Personalized life experience is the core;The overall experience of visitors is important.Based on this,the shortcomings of Shared accommodation is analyzed.Finally,Airbnb has provided numerous experience activities and brought new experience to tourists.However,the localization of the platform and the constant changes of the domestic tourism market havebrought new challenges and opportunities to it.In the daily operation and management,some elements will not be satisfied,which will affect the overall tourism experience.Therefore,on the basis of the results of factor analysis and in combination with the content related to tourism experience,tourists and Shared accommodation market,the internal experience level of houses and the related services provided by homestay are constantly improved,so as to improve the comprehensive experience level of tourists.This study can quickly and timely extract the elements of tourists' experience from the data of tourists' comments,which can provide effective reference opinions for home stay operators and related enterprises,and at the same time broaden the wide application of data mining technology.With the continuous innovation of the Internet,artificial intelligence and other technologies,the accommodation sector will continue to optimize and develop,which will create greater value for the sharing economy.