酒店评论数据有用性影响因素分析——以缤客网为例

[目的/意义]旨在为用户出行时酒店预订提供参考建议。[方法/过程]对缤客网上的酒店评论数据进行预处理,使用Python在Anaconda环境下实现评论文本的情感极性分析和主观性分析,并结合正面评论词数、负面评论词数、消费者此前评论数构建评论数据有用性影响因素模型,最后通过多元线性回归模型对评论数据有用性影响因素进行实证分析。[结果/结论]情感极性指标与评论有用性具有显著的相关性;正面评论词数和消费者此前评论数与评论有用性有一定程度的正面影响,这些影响因素能够帮助用户进行辅助决策。

Analysis on Influencing Factors of Hotel Comment Data Usefulness:Case Study of Booking.com

[Purpose/significance]The paper intends to provide references for hotel reservation in travelling time.[Method/process]Based on the preprocessing of the hotel comment data from Booking.com the paper used Python to realize the emotional polarity analysis and subjective analysis in Anaconda environment built a influencing factor model of the comment data usefulness combined with the number of positive comment words the number of negative comment words and the number of previous comments of consumers and finally made a empirical study on the influencing factors of comment data usefulness through multiple linear regression model.[Result/conclusion]The results show that the emotional polarity index has a significant correlation with the usefulness of comments and the number of positive comments’words and the number of previous comments words of consumers have a positive impact to some extent and the influencing factors help users to make auxiliary decisions.