活力
背景(考古学)
一致性(知识库)
数据科学
顾客满意度
订单(交换)
旅游
情绪分析
大数据
计算机科学
服务(商务)
业务
营销
万维网
知识管理
地理
数据挖掘
人工智能
考古
物理
财务
量子力学
作者
Mariana Cavique,Ricardo Ribeiro,Fernando Batista,Antónia Correia
标识
DOI:10.1080/13683500.2022.2115877
摘要
Given Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest’s satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: ‘host’s service’, ‘physical aspects’, and ‘location’. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that ‘physical aspects’ is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research.
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