An online reviews-driven large-scale group decision making approach for evaluating user satisfaction of sharing accommodation

Abstract

To promote the development of the peer-to-peer (P2P) accommodation in sharing economy, it is important to understand and ensure the determinants of high-level user satisfaction. Focusing on factors that affect the travel experience of P2P accommodation users, this article proposes a large-scale group decision making (LSGDM) based method with online reviews to evaluate user satisfaction of sharing accommodation. Firstly, the user demands (UDs) reflecting the actual concerns of users are extracted by combining negative and positive reviews from P2P accommodation platform Airbnb, where negative reviews are classified by sentiment analysis, TF-IDF and Word2Vec technology are used for extraction and the further verification of UDs from all online reviews is performed. Secondly, the level of satisfaction is evaluated through online responses from P2P accommodation users in a large-scale group of decision makers. Thirdly, the final degrees of satisfaction are ascertained by the proposed LSGDM approach, which includes subgroup clustering and a minimal variance weights based feedback mechanism for fair weights allocation under the condition of reaching consensus. Finally, the conclusions further serve as references for improving the performance of P2P sharing accommodation.