Research On The Improvement Of Passenger Satisfaction After Flight Delay Based On Online Reviews

Frequent flight delays and improper service remedies lead to low passenger satisfaction after delay.At the same time,passenger satisfaction affects the competitiveness of airlines to a certain extent.Airlines can reduce the negative impact of flight delays by adopting rich and targeted remedial services,and save the airline’s reputation and customer losses.Therefore,it is necessary to find the factors that affect passenger satisfaction after flight delays and improve service remedial measures.This is very necessary for airlines.Secondly,most of the current research uses structured data as the research object to analyze the influencing factors of passenger satisfaction after flight delay.There are few articles on passenger satisfaction after flight delay through unstructured data such as text comments.Due to the development of the Internet,online comments provide a new data source for the study of passenger satisfaction.Finally,the development of related research methods such as text mining has laid a technical support for the study of huge amounts of online review data.Therefore,this thesis takes the online reviews of the Skytrax website as the research object,digs the influencing factors of passenger satisfaction after flight delay,and explores the relationship between the influencing factors and passenger satisfaction,so as to better help airlines formulate corresponding remedial measures and improve Passenger satisfaction after delays,which in turn enhances the competitiveness of airlines.This thesis firstly performs jieba word segmentation on the passenger comments after the delay,and finds the keywords of the comments by calculating the TF-IDF.Then use Word2 Vec to train the segmented text,and map each word to the word vector space.Then,the K-means algorithm is used to cluster the distance of keywords in the word vector space.The number of clusters is determined by the silhouette coefficient.Finally,the keywords under the same theme of the cluster are refined into the passenger satisfaction degree after delay influencing factors.The study identified six influencing factors,including meals and rest,words and deeds of service staff,compensation in cash or vouchers,formalities,information services,and time to resume flights.At the same time,Semantic Network Analysis was used to identify the semantic connections and differences between the keywords of passenger satisfaction in different scenarios,and then found their different concerns.Using machine learning and other methods to calculate the sentiment value of text language,mining text opinions and attitudes,and using the level of sentiment value as the degree of passenger satisfaction after delay,perform sentiment analysis on documents with different granularities.Finally,by constructing a matrix of influencing factors of passenger satisfaction,and using grey relational analysis and logit regression,the correlation strength of each influencing factor and satisfaction in different scenarios and the significance of improving the service level on passenger satisfaction are obtained.Based on the research results,this thesis proposes that catering and accommodation arrangements should be provided to delayed passengers in a timely manner to ensure passengers’ rest;improve the service level of employees after flight delays;improve the compensation system for flight delays,and provide passengers with appropriate cash or voucher compensation;The company’s system of refund and rebooking improves the level of ticketing service after delay;strengthens the collection of information on flight delays and go-arounds and releases them in a unified and timely manner;coordinates the cooperation between various departments to resume flights in a timely manner and other management suggestions.