Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Abstract

Purpose There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers. Design/methodology/approach Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio. Findings The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative. Research limitations/implications The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study. Originality/value Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

User Experience of Mobile Banking Application in Indonesia: New Technology of Banking

“…The prevalence of negative terms such as 'error,' 'bad,' and 'bug' in the top 60 most frequent terms underscores areas of user dissatisfaction, a sentiment echoed in other studies that utilize keyword frequency analysis to gauge user satisfaction. This finding corroborates earlier studies that highlight the impact of negative reviews on user satisfaction (Trivedi et al, 2022).…”

“…Conversely, Permata Mobile X by Permata Bank received the lowest rating among the apps surveyed. Thus, negative reviews can significantly impact user satisfaction and, by extension, the reputation of a mobile banking application (Oh & Kim, 2021;Trivedi et al, 2022).…”

An Analysis of Customer Textual Reviews and Satisfaction at Luxury Hotels in Singapore’s Marina Bay Area (SG-Clean-Certified Hotels)

“…Customers tend to use a variety of words in order to describe several positive aspects of hotel products and services [58]. Diversity, as used in this study, refers to the redundancy of words in online reviews.…”

“…The use of fewer redundant words in customer reviews is associated with higher diversity. Customers describe hotel products and services in a variety of ways, which results in a diversity of words in positive reviews [20,58].…”

“…Prior to making any purchase decision, perusing product or services' online reviews on the internet is an essential step [60]. Consequently, the popularity of hotels depends on online reviews posted after customers stay, since a large number of negative reviews can easily undermine a hotel [58]. The dependent variable in this study is customers' overall satisfaction and the independent and dependent variables are sentiment polarity, diversity, readability, and word length.…”