Sentiment drivers of hotel customers: a hybrid approach using unstructured data from online reviews

Abstract

PurposeWith the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share their experiences, as well as reading online review before making a hotel reservation. Hotel customers considered it as reliable source and it influences customers' hotel selection. Most of these reviews reside in unstructured format, scattered across in the Internet and inherently unorganized. The purpose of this study was to use predictive text analytics to identify sentiment drivers from unstructured online reviews.Design/methodology/approachThe research used sentiment classifications to analyze customers' reviews on hotels from TripAdvisor. In total, 9,286 written reviews by hotel customers were scrapped from 442 hotels in Malaysia. A detailed text analytic was conducted and was followed by a development of a theoretical framework based on the hybrid approach. AMOS was used to analyze the relationship between customer sentiments and overall review rating.FindingsWith the use of Structural Equation Modeling (SEM) and clustering technique, a list of sentiment drivers was detected, i.e. location, room, service, sleep, value for money and cleanliness. Among these variables, service quality and room facilities emerged as the most influential factors. Sentiment drivers obtained in this study provided the insights to hotel operators to improve the hotel conditions.Research limitations/implicationsAlthough this study extended the existing literature on sentiment analysis by providing valuable insights to hoteliers, it is not without its limitations. For instance, online hotel reviews collected for this study were limited to one specific online review platform. Despite the large sample size to support and justify the findings, the generalizability power was restricted. Thus, future research should also consider and expand to other type of online review channels. Therefore, a need to examine these data reside various social media applications, i.e. Facebook, Instagram and YouTube.Practical implicationsThis study highlights the significance of hybrid predictive model in analyzing the unstructured hotel reviews. Based on the hybrid predictive model we developed, six sentiment drivers emerged from the data analysis, i.e. location, service quality, value for money, sleep quality, room design and cleanliness. This consideration is critical due to the ever-increasing unstructured data resides in the online space. This explores the possibility of applying data analytic technique in a more efficient manner to obtain customer insights for hotel managerial consideration.Originality/valueThis study analyzed customer sentiments toward the hotel in Malaysia with the use of predictive text analytics technique. The main contribution was the list of sentiment drivers and the insights needed to improve the hotel conditions in Malaysia. In addition, the findings demonstrated motivating findings from different methodological perspective and provided hoteliers with the recommendation for improved review ratings.

Social media usage in business decision-making: the case of Malaysian small hospitality organisations

“…Companies can collect data from social media platforms and develop intelligence regarding the competitive environment (Ho et al, 2020). Social media analytics, i.e.…”

“…However, leveraging social media data is contingent upon a company's capability to capture, interpret and fit a predictive model in its decision-making process (Fan and Gordon, 2014;Stieglitz et al, 2018). Seemingly simple data such as "like" and "subscribe" figures are raw data points for insights, which can provide useful knowledge and evidence for decisionmaking if properly interpreted (Ho et al, 2020;Macht and Chapman, 2019). Fan and Gordon (2014) categorised the social media analytics process into three stages:…”

“…The insights from the literature showed that businesses leverage social media as a means for marketing-related functionalities, i.e. image building and promotion (Ho et al, 2020;Aluri et al, 2015), and customer engagement, i.e. SCRM (Prasad et al, 2017).…”

“…The advent of social media, with its user-generated content (UGC) (i.e. secondary data), allows the collection of real-time data to derive both predictive and prescriptive models to support decisions and forecast business outcomes, hence resulting in faster and more relevant decisions, thus strengthening business competitiveness (Sloan and Quan-Haase, 2017;Ho et al, 2020). However, obtaining optimum leverage from social media involves investment in financial and human resources as the data from various platforms (such as Facebook or YouTube) are mainly unstructured raw data that must be transformed before analysis can be conducted.…”

Tourist Tax and Ratings of Online Reviews

“…As online ratings have become a crucial component in the accommodation sector, a large number of studies have focused on what factors influence online ratings most. Besides obvious factors related to the quality of the service provided by hotels (see Ho et al, 2020), the extraordinary amount of data has allowed researchers to focus on possible determinants which are usually not taken into account. See, for instance, Gao et al (2018), that look at how cultural factors such as "power distance" are reflected in ratings posted on TripAdvisor.…”