Research On Evaluating And Improving Customer Satisfaction At XC Hotel Based On Online Reviews

With the rapid development of information technology and the widespread popularity of social media platforms,online hotel booking has gradually become the dominant consumer trend.Compared with traditional offline booking,online booking has significant advantages in price transparency and diversified selection,which has attracted a large number of consumers.Although hotel services continue to improve and consumer bargaining power is increasing,some customer demands still remain unsatisfied,leading to a gradual decline in brand loyalty.In addition,an increasing number of consumers prefer to express their opinions and share their experiences through online platforms,most of which include user attitudes and emotional information.These pieces of information are of great value in product comparison,recommendation,and public opinion tracking and monitoring.Therefore,how to effectively use numerous online reviews,deeply analyze the key factors affecting customer satisfaction,improve customer satisfaction to enhance customer loyalty,has become an important issue for the development of the hotel industry.Building on theories of customer satisfaction and perceived value,this paper explores how text mining and sentiment analysis can be used to evaluate customer satisfaction at XC Hotel in the context of the growing demand for quality tourism.The study uses online review data to extract features using TF-IDF,Word2Vec,and K-means algorithms.BP neural network and weight matrix are used to determine the primary factors that affect customer satisfaction.The study then employs a BERT and RF fusion model for sentiment polarity classification of the reviews.Finally,a multiple linear regression model is developed to assess custoper satisfaction,calculate satisfaction scores for primary and secondary factors,and validate the effectiveness and rationality of the proposed model.The study results showed that the fusion model based on BERT and RF proposed in this paper has certain advantages in acc.uracy,precision,and F1 value compared with RNN,LSTM,and Bi-LSTM models.In addition,three key factors affecting customer satisfaction were identified through BP neural network and weight matrix:service,facilities,and value perception.Finally,through the customer satisfaction model,eight secondary factors that scored lower than the overall satisfaction score of XC hotel were identified,including sound insulation,bathrooms,gym,laundry room,lobby,decoration,robots,and network,and specific improvement suggestions were proposed for these factors.This paper enriches the method of evaluating hotel customer satisfaction and provides new management ideas for hotel managers.At the same time,the research results help XC hotel to stand out in fierce market competition and achieve sustainable development.