What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

Abstract

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists' behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to "live like a local" through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers' personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers' opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect.

Diversity and Inclusion in the Sharing Economy: An Airbnb Case Study

“…For example, user satisfaction with the SE service has been studied extensively, by means of statistical and linguistic analysis of ratings and reviews (Zervas, Proserpio, and Byers 2015;Zhu, Lin, and Cheng 2020;Santos et al 2020;Bridges and Vásquez 2018;Alsudais and Teubner 2019). Several studies have ensued that have gone beyond sentiment analysis and looked into what participants in this hospitality service care the most about (Luo and Tang 2019;Sutherland and Kiatkawsin 2020;Joseph and Varghese 2019;Lee et al 2020;Cheng and Jin 2019;Luo 2018;Quattrone et al 2020). Business-oriented aspects such as property location, facilities/amenities, and communication with the hosts were consistently found to be particularly important in all studies.…”

Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience

“…These analyses can be conducted either separately or in conjunction with the feature-based classification [10]. Mostly, this category of analyses is based on a manual creation of a sentiment lexicon via unsupervised labeling of words or phrases or using online resources like WordNet [11], NRC Emotion lexicon [12], SentiWord Net [13] with their sentiment polarity and subjectivity status [14], [15]- [21]. The sentiment labels typically represent binary classification or a multi-point scale measuring the degree of polarity of expression and emotions.…”

“…were examined. In the evaluations, the following measures were used: polarity strength [59]; subjective and comparative features importance [60]; composite score for a specific product by including star rating, number of positive reviews, number of negative reviews, helpfulness score of reviews, review age [61]; weight that customers place on individual product features and the polarity and strength of the underlying evaluations [62]; latent weights of aspect (topic) for individual reviews [12]. In studies [63]- [67], the level of satisfaction/dissatisfaction by specific factors of hotel products and services based on the evaluation of positive and negative reviews is introduced.…”

Do travelers' reviews depend on the destination? An analysis in coastal and urban peer‐to‐peer lodgings

“…Law et al (2014) note that social media develop a significant role in tourists' decisionmaking given the social dimension of behavior in this hospitality context. In 2017, Statistic Brain revealed that 81% of travelers find user reviews important (Luo, 2018). Although the valence (and influence) of UGC in and on review helpfulness, consumer attitude, and behavior show diverging results (cf.…”