Hotel Reviews Research Articles

“Most Americans like their privacy.” Exploring privacy concerns through US guests’ reviews

PurposeThis study aims to explore US hotel guests’ privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly discussed by guests in their reviews and to examine the influence of cultural proximity on privacy concerns.Design/methodology/approachThis study combined automated text analytics with content analysis. The database consisted of 68,000 hotel reviews written by US guests lodged in different types of hotels in five European cities. Linguistic Inquiry Word Count, Leximancer and SPSS software were used for data analysis. Automated text analytics and a validated privacy dictionary were used to investigate the reviews by exploring the categories, themes and attributes of privacy concerns. Content analysis was used to analyze the narratives and select representative snippets.FindingsThe findings revealed various categories, themes and concepts related to privacy concerns. The two most commonly discussed categories were privacy restriction and outcome state. The main themes discussed in association with privacy were “room,” “hotel,” “breakfast” and several concepts within each of these themes were identified. Furthermore, US guests showed the lowest levels of privacy concerns when staying at American hotel chains as opposed to non-American chains or independent hotels, highlighting the role of cultural proximity in privacy concerns.Practical implicationsHotel managers can benefit from the results by improving their understanding of hotel and service attributes mostly associated with privacy concerns. Specific suggestions are provided to hoteliers on how to increase guests’ privacy and on how to manage issues related to cultural distance with guests.Originality/valueThis study contributes to the hospitality literature by investigating a neglected issue: on-site hotel guests’ privacy concerns. Using an unobtrusive method of data collection and text analytics, this study offers valuable insights into the categories of privacy, the most recurrent themes in hotel guests’ reviews and the potential relationship between cultural proximity and privacy concerns.

Machine learning for assessing quality of service in the hospitality sector based on customer reviews

The increasing use of online hospitality platforms provides firsthand information about clients preferences, which are essential to improve hotel services and increase the quality of service perception. Customer reviews can be used to automatically extract the most relevant aspects of the quality of service for hospitality clientele. This paper proposes a framework for the assessment of the quality of service in the hospitality sector based on the exploitation of customer reviews through natural language processing and machine learning methods. The proposed framework automatically discovers the quality of service aspects relevant to hotel customers. Hotel reviews from Bogotá and Madrid are automatically scrapped from Booking.com. Semantic information is inferred through Latent Dirichlet Allocation and FastText, which allow representing text reviews as vectors. A dimensionality reduction technique is applied to visualise and interpret large amounts of customer reviews. Visualisations of the most important quality of service aspects are generated, allowing to qualitatively and quantitatively assess the quality of service. Results show that it is possible to automatically extract the main quality of service aspects perceived by customers from large customer review datasets. These findings could be used by hospitality managers to understand clients better and to improve the quality of service.

Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams.

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter of the Poisson distribution that yields the best value for . By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Studying the relationship between human resources practices, employee motivation, and online hotel reviews: An empirical approach to the hospitality industry

Increasing competition in hospitality is one of the industry challenges, which depends mainly on human capital. Thus, this research investigated the relationship between human resources practices, employee motivation, and online hotel reviews. Hypotheses were tested using two-phase survey data collected from 308 respondents corresponding to 154 dyads of frontline employees and human resources managers from hotels located in Brazil and England. Quantitative online hotel review ratings were collected from the surveyed hotels as performance indicators. Partial least squares structural equation modeling was used to examine the data collected. Employee intrinsic motivation negatively influenced online hotel review ratings. The online hotel review overall ratings were positively affected by extrinsic motivation. Likewise, human resources practices had a strong influence on the online hotel review service ratings. This study presented some insights to hospitality organizations, identifying relationships among three important topics on hotel management. This information can help hoteliers develop better competitive strategies, using human resources to increase online hotel review ratings, considered critical hotel performance indicators.

Differences in Chinese and Western tourists faced with Japanese hospitality: a natural language processing approach

Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan's biggest tourist populations, Chinese and Western tourists. However, with ever-increasing customer populations, this is hard to accomplish without extensive customer base studies. There is a need for an automated method for identifying these expectations at a large scale. For this, we used a data-driven approach to our analysis. Our study analyzed their satisfaction factors comparing soft attributes, such as service, with hard attributes, such as location and facilities, and studied different price ranges. We collected hotel reviews and extracted keywords to classify the sentiment of sentences with an SVC. We then used dependency parsing and part-of-speech tagging to extract nouns tied to positive adjectives. We found that Chinese tourists consider room quality more than hospitality, whereas Westerners are delighted more by staff behavior. Furthermore, the lack of a Chinese-friendly environment for Chinese customers and cigarette smell for Western ones can be disappointing factors of their stay. As one of the first studies in the tourism field to use the high-standard Japanese hospitality environment for this analysis, our cross-cultural study contributes to both the theoretical understanding of satisfaction and suggests practical applications and strategies for hotel managers.

How Delightful is Indian Wellness Tourism? A Netnographic Study

The growing number of wellness care facilities in India has raised concern over the service quality that is being provided to the tourists. This research targets to explore the dimensions of wellness tourism service quality based on customers’ quality perception. Social media platforms such as Google reviews and hotel review blogs/websites were used to gather 400 public reviews. A Naïve Bayes machine learning Sentiment Analysis approach was used to identify critical areas to improve service delivery, customer relationship, and hospitality management in wellness resorts. Tangibility was identified as the most important dimension followed by empathy, assurance, reliability, and responsiveness. Assurance, empathy, and reliability have the most negative sentiments shared by tourists. Food quality, rooms and accommodation facilities, safety and security, attitude towards customer complaints, the behaviour of the staff, error-free services, and proper training are areas upon which Indian wellness resorts should focus. This study intends to identify additional constructs in future research and build robust models to actively rank the important factors for better customer engagement. Research findings may support managers and policymakers to identify areas of improvement to help them develop the wellness resorts in India.

Fake Review Prediction and Review Analysis

Online reviews can be deceptive or manipulative evaluations of services and products which are often carried out deliberately for manipulation strategy to mislead the readers. Identifying such reviews is an important but challenging problem. There are even some associations in the merchandise industry who are hiring professionals to write fake reviews so that they can promote their products or defame rivals products. Hence we aim to develop a method which will detect fake reviews and remove them. The proposed method classifies users’ reviews into suspicious, fake, positive and negative categories by phase-wise processing. In this paper, we are processing hotel reviews by using different data mining techniques. Moreover the reviews obtained from users are being classified into positive or negative which can be used by a consumer to select a product. Organizations providing services can monitor customer sentiments by scrutinizing and understanding what the customers are thinking about products through reviews. This can help buyers to purchase valuable products and spend their money on quality products. Also in our model end users see star ratings based on reviews for each hotel.

Let Photos Speak: The Effect of User-Generated Visual Content on Hotel Review Helpfulness

A picture is worth a thousand words. User-generated photos (UGPs) are increasingly accompanying online reviews of hotels. This article draws on media richness theory to estimate the effects of UGPs on hotel reviews’ helpfulness. Based on a sample of 1,159,590 valid reviews with 464,316 photos, we utilized an integrated analytical model incorporating both econometric analyses and image-processing techniques. The results show that reviews accompanied by UGPs are generally rated as more helpful than those with textual content only. Furthermore, photos showing guestroom objects were rated as more helpful than those showing food & beverages. Finally, the positive effects of UGPs on review helpfulness were especially prominent for hotels with lower prices and negative reviews. This study adds new insights to the online review literature and advances the methodological approach in analyzing unstructured user-generated content. This study provides important implications for hotel managers and online booking platforms regarding UGP management.

Predicting hotel reviews from sentiment: a multinomial classification framework

PurposeMachine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.Design/methodology/approachWell-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.FindingsBDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.Originality/valueTo the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

Predicting Veracity in Online Hotel Reviews Using Types of Reported Speech, Speaker Identity, and Quotation Marks

Predicting Veracity in Online Hotel Reviews Using Types of Reported Speech, Speaker Identity, and Quotation Marks

A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews

论可推广性的情感分析法以创建酒店字典：以TripAdvisor酒店评论为样本的大数据分析摘要研究目的对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。研究设计/方法/途径研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典（有配重的词典L1和手动选择的词典L2）, 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法（公共词典法和复杂机器学习算法）来对比词典的准确度。研究结果精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。研究实际启示本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。研究原创性/价值本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

The Influence of Sustainable Initiatives on Hotel Guest Experience

This study investigates the relationship between sustainable initiatives in hotels and their guest experiences by using big data from online guest reviews. Hotel review data of 1 018 938 hotel guest reviews from 23 international destinations were used. Word recognition software was utilised to filter reviews containing comments about sustainable initiatives resulting in a usable database of 903 reviews. Sentiment analysis was then performed to enable the transformation of the variables into 5-point Likert type scales. Using chi-squared tests and Cramer’s V for statistical and practical significance respectively, sustainable initiatives were found to have a statistical significant relationship with each of the eight hotel experience dimensions as well as overall hotel experience. The eight hotel experience dimensions are value, service, location, rooms, food, ambience, facilities and cleanliness. The hypothesis was accepted as there was a statistically significant relationship between sustainable initiatives and the hotel experience dimensions. The results from the comments, provide hotel management and academics with new insight into the relationship between sustainable initiatives in hotels and the hotel guest experience. Sustainable initiatives should be considered as another important dimension of the hotel experience, and big data analysis is effective to analyse guest reviews.

Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier

In the hotel tourism sector, of course, it cannot be separated from the role of social media because tourists tend to share experiences about services and products offered by a hotel, such as adding pictures, reviews, and ratings which will be helpful as references for other tourists, for example on the media online TripAdvisor. However, tourists' many experiences regarding a hotel make some people feel confused in determining the right hotel to visit. Therefore, in this study, an aspect-based analysis of reviews on hotels is carried out, which will make it easier for tourists to determine the right hotel based on the best category aspects. The dataset used is the TripAdvisor Hotel Reviews dataset which is already on the Kaggle website. And has five aspects, namely Room, Location, Cleanliness, Registration, and Service. A review analysis was carried out into positive and negative categories using the Random Forest, SVM, and Naive Bayes based Hybrid Classifier methods to solve this problem. In this study the Hybrid Classifier method gets better accuracy than the classification using one algorithm on multi-aspect data, namely the Hybrid Classifier got an average accuracy 84%, Naïve Bayes got an average accuracy 82.4%, Random Forest got an average accuracy 82.2%, and use SVM got an average accuracy 81%

Sentiment classification based on weak tagging information and imbalanced data

Sentiment classification aims to solve the problem of automatic judgment of sentiment polarity. In the sentiment classification task of text data, such as online reviews, traditional deep learning models are dedicated to algorithm optimization but ignore the characteristics of imbalanced distribution of the number of classified samples and the inclusion of weak tagging information such as ratings and tags. Based on the traditional deep learning model, the method of random oversampling and cost sensitivity is used to increase the contribution of a minority of samples to the model loss function and avoid the model biasing to the majority of samples. The model training is divided into two stages. In the first stage, a large amount of weak tagging data is used to train the model, therefore a model that captures the sentiment semantics of the data is obtained. After that, the model parameters trained in the first stage are used as the initial parameters of the second stage model training, and only a small amount of tagging data is used to continue training the model to reduce the impact of noise, thus reducing the use of manual tagging samples. The experimental results show that the method is considerably better than traditional deep learning models in the sentiment classification task of hotel review data.

Assessment of Indoor Environmental Quality in Budget Hotels Using Text-Mining Method: Case Study of Top Five Brands in China

Guests’ evaluation of indoor environmental quality (IEQ) is important for identifying environment quality problems in hotels and improving service quality. This paper aims to identify IEQ problems in budget hotels in China and improve them. Specifically, 2.06 million online reviews of budget hotels were used to assess IEQ issues in China’s budget hotels in four areas: acoustic environment, luminous environment, indoor air quality (IAQ) and thermal environment. The influences of the season, region and type of customers on the IEQ evaluation were also explored, and the main causes of IEQ problems were also identified. The research results show that the IEQ complaint rates of budget hotels are relatively high. In particular, complaints about the acoustic environment are more common. Differences in seasons and climate zones have significant effects on complaints about the acoustic environment, thermal environment and IAQ. Different types of customers have different concerns about hotel IEQ, among which solo travelers and traveling couples have higher requirements for IEQ. The occurrence of IEQ problems significantly reduces a hotel’s online rating, with IAQ and the thermal environment having the greatest impacts, but the causal factors that trigger IEQ problems are relatively concentrated. The findings of this paper can provide a reference for assessing IEQ problems in hotel buildings and guide hotel managers to adopt targeted IEQ improvement programs to promote sustainable development in the hotel industry.

Dealing with pandemics: An investigation of the effects of COVID-19 on customers' evaluations of hospitality services.

The hospitality industry is highly vulnerable to pandemics. However, little is known about how pandemics alter travelers' evaluations of hospitality services. Therefore, this study investigates the changes in travelers' expectations and perceptions of hotel services during different stages of the novel coronavirus 2019 (COVID-19) pandemic. 98,163 Chinese hotel reviews were collected and scrutinized via text mining and sentiment analysis techniques to derive new implications for service optimization. The results reveal shifts in consumers’ evaluations well beyond hygienic requirements. Insights obtained from this research can help guide hospitality practice in organizing its priorities during acute pandemic situations and adjusting to possibly longer-lasting shifts in consumer preferences.

The relationship between price paid and hotel review ratings: Expectancy-disconfirmation or placebo effect?

Whilst there is extant research focusing on the link between hotel reviews and pricing there is scant attention paid to the impact of pricing on hotel review ratings. This paper presents a large scale study based on a novel dataset of more than 44,000 guest review ratings linked to the prices paid for rooms of an European hotel group. Through applying a panel regression analysis we reveal that expectancy-disconfirmation is generally stronger than the placebo effect and higher prices have a negative effect on review ratings, not only when assessing perceived value for money but also when evaluating perceived quality. We identify the trip and guest characteristics which impact on the price-review relationship. Findings provide hotel managers with key quantitative information for simultaneously calibrating quality and revenue targets. Substantial implications include an integrated approach for segmentation as a route to better manage pricing, revenue optimization and reputation. • A unique dataset links individual prices paid with review ratings by hotel guests. • Higher prices have a negative impact on perceived value and perceived quality. • Expectancy-disconfirmation is generally stronger than the placebo effect. • Specific strategies are outlined for hotel revenue management. • Application of panel regression as an analytical tool.

COVID-19, social distancing, and risk-averse actions of hospitality and tourism consumers: A case of South Korea

The COVID-19 pandemic has significantly changed individuals' daily life due to increased risk aversion, which has affected their consumption patterns and preferences. To understand the effect of the pandemic on consumer behavior through risk aversion, this study investigated the relationships among the pandemic, social distancing, online information search, and firm performance in the hospitality and tourism industries. For data analysis, we developed two joint models and estimated the models using the fixed-effects method. The results of the first model showed that social distancing triggered by COVID-19 news stories affected firm value. The second regional-level analysis revealed that the number of confirmed cases and COVID-19 news stories influenced individuals’ social distancing and online information search for tourist attractions and the changed social distancing and online search, in turn, affected the volume of online hotel reviews.

Research on the Role of Influencing Factors on Hotel Customer Satisfaction Based on BP Neural Network and Text Mining

With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels.

Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis.

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.