本文以河北省民宿的地理位置坐标数据以及携程网上的河北省民宿评论数据为研究对象,分别从地理空间和情感满意度两个角度对民宿进行研究。首先,利用最邻近分析、核密度分析等多种空间分析方法对民宿的空间分布特征进行探讨,得出河北省...本文以河北省民宿的地理位置坐标数据以及携程网上的河北省民宿评论数据为研究对象,分别从地理空间和情感满意度两个角度对民宿进行研究。首先,利用最邻近分析、核密度分析等多种空间分析方法对民宿的空间分布特征进行探讨,得出河北省民宿的分布特征。接着,利用LDA-LSTM模型对民宿评论文本数据进行分析,将LDA主题提取模型、Word2Vec词向量化以及Pagerank算法进行结合,实现对民宿主题词的二次挖掘。最后,结合LSTM神经网络模型,计算每个主题的满意度,并对影响住户满意度的因素进行具体分析。This article takes the geographic coordinates of homestays in Hebei Province and the review data of homestays in Hebei Province on Ctrip as the research objects, and studies homestays from two perspectives: geographic space and emotional satisfaction. Firstly, various spatial analysis methods such as nearest neighbor analysis and kernel density analysis are used to explore the spatial distribution characteristics of homestays, and the distribution characteristics of homestays in Hebei Province are obtained. Next, the LDA-LSTM model is used to analyze the text data of homestay comments. The LDA topic extraction model, Word2Vec word vectorization, and Pagerank algorithm are combined to achieve secondary mining of homestay topic words. Finally, the LSTM neural network model is combined to calculate the satisfaction of each topic and analyze the factors that affect household satisfaction.展开更多
文摘本文以河北省民宿的地理位置坐标数据以及携程网上的河北省民宿评论数据为研究对象,分别从地理空间和情感满意度两个角度对民宿进行研究。首先,利用最邻近分析、核密度分析等多种空间分析方法对民宿的空间分布特征进行探讨,得出河北省民宿的分布特征。接着,利用LDA-LSTM模型对民宿评论文本数据进行分析,将LDA主题提取模型、Word2Vec词向量化以及Pagerank算法进行结合,实现对民宿主题词的二次挖掘。最后,结合LSTM神经网络模型,计算每个主题的满意度,并对影响住户满意度的因素进行具体分析。This article takes the geographic coordinates of homestays in Hebei Province and the review data of homestays in Hebei Province on Ctrip as the research objects, and studies homestays from two perspectives: geographic space and emotional satisfaction. Firstly, various spatial analysis methods such as nearest neighbor analysis and kernel density analysis are used to explore the spatial distribution characteristics of homestays, and the distribution characteristics of homestays in Hebei Province are obtained. Next, the LDA-LSTM model is used to analyze the text data of homestay comments. The LDA topic extraction model, Word2Vec word vectorization, and Pagerank algorithm are combined to achieve secondary mining of homestay topic words. Finally, the LSTM neural network model is combined to calculate the satisfaction of each topic and analyze the factors that affect household satisfaction.
