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The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.

Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.

The purpose of this study is to learn about data structures in relation to decisions about family travel being influenced by sellers (i.e. agents), spouses/partners, spouse, kids and friends/relatives (i.e. influencing units). Concern is with units affecting decisions about duration, budget, destinations, restaurants, daily tours and accommodation facilities.

Research is on survey data collected from British family groups visiting Turkey in the summer of 2007. Data allowing study of a range of matters were collected by haphazard but not necessarily random selection of respondents in an airport departure lounge.

The study identifies independent effects of influencing units on aspects of decision but also shows that demographic factors affect aspects of decision making such as the choice of destination. Methodologically, the study identifies questionnaire structure and analysis methodology to use in examining further research.

Results suggest how influencing units (e.g. self, spouse) affect aspects of decision making aiding practitioners in understanding influences on decisions.

The research opens a new area of investigation by implementing a questionnaire structure, developing analysis avenues and establishing results for further examination (i.e. to establish their generality).

Consumers’ evaluation of online review helpfulness has been widely examined. The extant literature suggests that the attributes of review content (e.g. review length and extremity) influence review helpfulness. However, review length cannot fully reflect the richness of the review content. Anchoring on information diagnosticity and extremity bias, this study aims to explore the effect of review comprehensiveness on its helpfulness.

Field observations were obtained from 11,812 online restaurant reviews on a popular restaurant review platform. A controlled experiment was conducted to further delineate the effect of review comprehensiveness.

Review comprehensiveness moderates the effects of review length and an extremely negative review on helpfulness. It also confirms that for reviews of the same length, one covering more aspects is perceived by consumers as more helpful.

Different aspects of information in a review can efficiently assist decision-making. The results suggest that review platforms can better design their interface by providing separate areas for different product aspects. The platform can then receive more comprehensive and helpful reviews and increase the diagnosticity of these.

The study enriches the literature by introducing review comprehensiveness and examining the joint effects of review length and comprehensiveness on helpfulness. It also contributes to the literature by indicating how to reduce the effect of review extremity.

The purpose of this study is to explore the impact of other customer's opinion on a service firm and its alliance on the evaluation of the airline by the focal customer by integrating qualitative and quantitative user-generated content. The study also explores the relative importance of core and peripheral attributes in consumer evaluations.

A text mining and natural language processing-based approach was followed to extract insights from the qualitative part of 18,457 consumer reviews, which were later analyzed along with the quantitative information obtained from the reviews using linear regression and logistic regression methods.

The authors found that customer satisfaction and recommendation behavior is formed by own and others' opinion about the airline and alliance. The relative importance of the core and peripheral attributes depends on the psychological distance from the evaluation of the attribute.

The theoretical contribution and managerial implications have been discussed in detail.

It helps in review management strategy, service design strategy and the alliance and partnership strategies of the airlines.

This is the first paper that explores the impact attribute-level evaluations found in prior reviews on the future reviews of customers. It also explores the effect of prior reviews in the context of a service business and its alliances.

The purpose of this paper is to explore how rating mechanisms encourage emotional labor norms among sharing economy consumers.

This study follows a mixed-methods research design. Survey data from 207 consumers were used to quantify the impact of three distinct rating dimensions on a consumer behavioral outcome (emotional labor). In the second step, 18 focus groups with 94 participants were used to investigate the conditioning functions of ratings in more depth.

Rating mechanisms condition consumers toward performing socially desirable behaviors during sharing transactions. While consumers accept the necessity of bilateral rating mechanisms, they also recognize their coercive nature. Furthermore, the presence of bilateral rating mechanisms leads to negative outcomes such as annoyance and frustration.

This study contributes to sharing economy literature by examining bilateral rating mechanisms as a means of behavioral conditioning for consumers. This study points to improvements in platform design and informs theory on tripartite markets as well as trust.

The purpose of this paper is to investigate the impact of climate change rating organisations on rated firms, to understand whether disclosure ratings can facilitate enhanced emissions performance.

This study uses 1,848 cross-country firm-year observations from organisations that responded to the carbon disclosure project (the rater) between 2011 and 2015 and, hence, were rated for their disclosure. Drawing on the ideology of numbers, this paper hypothesises that the disciplinary power of ratings will result in rated firms improving their subsequent disclosure scores. Following the environmentally-friendly ideology, this study hypothesises that poorly-rated firms will adopt decoupling behaviour, by improving their climate change disclosure scores without reducing the intensity of their greenhouse gas (GHG) emissions.

The results indicate that climate change disclosure ratings pressure poorly-rated firms to improve their disclosure scores in subsequent years, yet these firms are not inclined to lower their GHG emissions. Further, the direct publication of firms’ GHG emissions intensity can exert some restricted disciplinary impact on rated firms, as the more polluting firms tend to improve their subsequent climate change performance compared with those having lower emissions levels.

This paper argues that the ability of corporate sustainability rating schemes to influence corporate behaviour comprehensively is limited and should be used with caution.

This paper sheds new light on the ideological dynamics at play between the rater and the rated, while highlighting new aspects of the power-rating nexus in the climate change arena.

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.

The purpose of this paper is to propose and introduce a new classification model to segment a nation’s cultural tourists based on their motivations to travel to a military music festival. Little research is apparent about the types of people, and their motivations, who attend these types of festivals. In addition, the research investigates the impact of military music festivals on the concepts of patriotism and national identity.

The research approach involves empirical testing of a Canadian audience attending the Royal Nova Scotia International Tattoo, a longstanding annual musical event held in Nova Scotia, Canada, that pays tribute to the country’s military heritage. A proposed classification model that includes two dimensions is applied, which investigates: motivation to attend the event and kinship to Canada’s military and naval traditions.

Findings provide a better understanding of the diversity of the Canadian cultural tourist audience attending a military music display in terms of tourists’ demographics, experience of the show and the desire to return. This research also provides new insights as to the ability of a military musical event to arouse emotions of national pride, patriotism and strengthen national identity.

This research is important to event sponsors and organizers of military music events as they attempt to maintain productivity and attendance growth in an increasingly competitive entertainment environment.