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Purpose: This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry. Design/methodology/approach: This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants. Findings: The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition. Research limitations/implications: These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry. Originality/value: This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications. [ABSTRACT FROM AUTHOR]

Purpose: Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory. Design/methodology/approach: Using HowNet's word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China. Findings: The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism. Research limitations/implications: The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews. Originality/value: To the best of the authors' knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality. [ABSTRACT FROM AUTHOR]

Smart tourism is receiving growing attention among scholars and practitioners as it advances tourism development and enhances travel experience. Existing research focuses on various technological applications and opinions from users and suppliers. However, discussions from the perspective of academic experts on smart tourism are scarce. In addition, most studies focus on only a single aspect of either people, planning process, or technological components of smart tourism. On the basis of the people–process–technology (PPT) framework and in-depth interviews with tourism academic experts, this study argues that the successful development and implementation of smart tourism involves the integration of and interactions among people, process, and technology. Such insights can contribute to the current literature in understanding smart tourism management and the relevant practices with a collaborative mechanism combining all these components. [ABSTRACT FROM AUTHOR]

This study investigates regional similarities and differences in the tourism research knowledge domain, based on two decades of publications. The authors analysed co-citations in the reference lists of full-length articles published in Annals of Tourism Research (1998–2017) in relation to four regions: North America, Europe, Asia, and the Pacific and Oceania. The findings reveal a degree of regional fragmentation in the tourism research knowledge domain. Several wide discourses characterize the scope of tourism knowledge: authenticity and the tourist experience, backpacker tourism, consumer behaviour, destination life cycle, tourism impacts and resident attitudes, paradigms, performance approach, and volunteer tourism. The authors document the contribution of each region to these discourses. Implications of regional distinctions are discussed along with future research opportunities. [ABSTRACT FROM AUTHOR]

Purpose: Despite a significant focus on customer evaluation and sentiment analysis, limited attention has been paid to discrete emotional perspective in terms of the emotionality used in text. This paper aims to extend the general-sentiment dictionary in Chinese to a restaurant-domain-specific dictionary, visualize spatiotemporal sentiment trends, identify the main discrete emotions that affect customers' ratings in a restaurant setting and identify constituents of influential emotions. Design/methodology/approach: A total of 683,610 online restaurant reviews downloaded from Dianping.com were analyzed by a sentiment dictionary optimized by the authors; the main emotions (joy, love, trust, anger, sadness and surprise) that affect online ratings were explored by using multiple linear regression methods. After tracking these sentiment review texts, Latent Dirichlet Allocation (LDA) and LDA models with term frequency-inverse document frequency as weights were used to find the factors that constitute influential emotions. Findings: The results show that it is viable to optimize or expand sentiment dictionary by word similarity. The findings highlight that love and anger have the highest effect on online ratings. The main factors that constitute consumers' anger (local characteristics, incorrect food portions and unobtrusive location) and love (comfortable dining atmosphere, obvious local characteristics and complete supporting services) are identified. Different from previous studies, negativity bias is not observed, which poses a question of whether it has to do with Chinese culture. Practical implications: These findings can help managers monitor the true quality of restaurant service in an area on time. Based on the results, restaurant operators can better decide which aspects they should pay more attention to; platforms can operate better and can have more manageable webpage settings; and consumers can easily capture the quality of restaurants to make better purchase decisions. Originality/value: This study builds upon the existing general sentiment dictionary in Chinese and, to the best of the authors' knowledge, is the first to provide a restaurant-domain-specific sentiment dictionary and use it for analysis. It also reveals the constituents of two prominent emotions (love and anger) in the case of restaurant reviews. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

This study explores the thematic structure of sustainability reports (SRs) of the hospitality and tourism (H&T) industry. The contents of the SRs were analyzed by combining text mining techniques and network analysis. The periodical analysis showed that sustainability reporting in the H&T industry is improving in terms of addressing a higher number of corporate social responsibility (CSR) issues across the period. Continental SRs indicated that although Europe is ahead of other regions, they all need to improve and present richer content to the report readers. The comparison of SRs, according to whether they were prepared with a specific reference to the GRI framework or not, also revealed some differences. Overall, this analysis highlights the H&T sector's strengths and weaknesses in terms of the topicality of its SRs and provides implications accordingly. [ABSTRACT FROM AUTHOR]

This study analyzes the intellectual structure of the sharing economy (SE) in the hospitality and tourism industry, starting from a sample of 189 papers. A co-citation analysis was performed on the 99 most frequently cited studies. The analysis carried out identified five clusters. These groups include the following: (i) the constituent elements of sharing, (ii) the SE and the sharing phenomenon, (iii) noncommercial website platforms and the social impact generated by sharing firms, (iv) economic impacts, and (v) some negative impacts. Each cluster is succinctly described, presenting the main theme and some subtopics. [ABSTRACT FROM AUTHOR]

There has been exponential growth in the power exercised by social media in hospitality and tourism. The power of social media platforms as stakeholders has been widely accepted by both academics and industry practitioners. However, to the best of the current authors' knowledge, there has been no conceptualization of the power attributable to social media. On this basis, it is both timely and necessary to establish theoretical grounds that explain the concept of social media power and its application in hospitality and tourism. A hierarchical model that characterizes social media power is constructed in the present article by bringing together fundamental power discourses, media effect theories, and technology determinism. The authors identify definitions and sources of social media power at different levels of the power pyramid and present various technological mechanisms that trigger such sources. This conceptual study proposes theoretical foundations for future research and theory-building. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China Tourism Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study critically examines, using a co‐citation approach, the evolution of food and culinary tourism research in the field of hospitality and tourism (H&T) from 1976 to 2019. A bibliometric study of publications indexed in the top 16 H&T journal databases was conducted, and a total of 523 food and culinary tourism‐related documents were identified and analyzed. The research findings revealed that food and culinary tourism publication numbers in H&T journals increased after 1999 yet somehow decreased after 2017. In terms of methodological approaches and data analysis, behavioral studies frequently used structural equation modeling, while advanced methodological approaches in other domains were deemed insufficient. The study findings further reveal that most of the influential works are relatively old and that no groundbreaking or game‐changing studies have occurred in recent years in food and culinary tourism research. Highlights: Analysis of journal categories reveals an increase in the number of articles published in H&T journals after 1999, reaching peak number in 2016. However, food and culinary tourism studies significantly declined in 2017 and attracted even fewer scholars in 2018 and 2019.Most of the influential works are relatively old.No groundbreaking or game‐changing developments occurred recently in food and culinary tourism research. [ABSTRACT FROM AUTHOR]

Using the "Computers are social actors" paradigm, this study brings the concept of power to human–computer interactions in tourism. Building on theories of social power and deliberate practice, the authors examine psychological effects of expert power of online travel review platforms (influencer) and its interaction effects with the power of users (influenced). Two expert platform attributes are conceptualized: specialization and experience. A significant interaction effect was identified between platform specialization, platform experience, and user power on perceived information-task fit using a 2 × 2 × 2 between-subjects experiment. When users are powerful, specialization affected perceived information-task fit for low-experience platforms; no significant effect was evident for high experience platforms. When users are powerless, specialization did not affect perceived information-task fit, regardless of experience condition. Perceived information-task fit mediated the effect of specialization on intention to use. The findings contribute to power discourses by exploring the workings of expert power. [ABSTRACT FROM AUTHOR]

This study aims to investigate the intellectual structure of P2P accommodation platforms through a co-citation analysis by using a social network approach. To this end, this paper analyzed 79 articles retrieved from the Scopus database. The findings show that there is a clear connection between the intellectual structures of P2P platforms and tourism journals. The cluster analysis identifies four groups, representing the intellectual structure of the P2P platforms. We discuss three relevant topics related to the theoretical pillars: the different levels of analysis, the diverse disciplines involved, and the increasing centrality gain by hospitality and tourism (H&T) studies. For each point, a future research agenda is proposed. [ABSTRACT FROM AUTHOR]

Purpose: This study aim to attempt to conceptualize agency in a hospitality setting and examine the psychological effects of agency-related visual cues on user perception and intention to use to understand the role of agency in the digitalization of hotel services. Design/methodology/approach: After developing demo videos of an express check-out application, two experiments were conducted to examine the effects of using an avatar and explain the psychological mechanism of how attributes of an avatar increase intention to use. Findings: Study 1 found that the presence of an avatar had a positive influence on intention to use. Study 2 retested the findings of Study 1 and illustrated the psychological mechanism of how two attributes of an avatar (social position and gender) influenced perceived expertise and intention to use. A significant interaction effect between social position and gender was found on perceived expertise. Perceived expertise also mediated the effect of an avatar on intention to use in the male avatar conditions. Originality/value: As the first attempt to investigate the role of avatars in human–computer interaction in a hotel setting, this study will serve as an example in testing the effects of agency-related technical features on user experience and behavioral intention, possibly broadening the current research scope of hospitality and tourism. This study also provides a useful guideline to develop and design a successful interface of digitalized hotel services. [ABSTRACT FROM AUTHOR]

Social media are acknowledged as an important information source that influences tourists' travel choices. However, qualitative studies that take an inductive approach to identify the roles of social media by investigating how social media affect travel choices are limited. By interviewing 21 tourists who had recently taken trips, this article aimed to identify the roles that social media played in the tourists' choices of six travel components (destination, transportation, accommodation, food and dining activities, attractions, as well as shopping and leisure activities). Four roles have been identified: Need Generator, Supporter, Guider and Approver. Theoretical and practical implications along with future research suggestions are discussed. [ABSTRACT FROM AUTHOR]

Using a mixed-method approach combining qualitative and quantitative review techniques, this study analyzed 86 articles to identify state-of-the-art thematic research trends and articulate knowledge domains in the smart tourism research. In particular, the thematic analysis identified 11 themes from the current smart tourism research. Results of the co-citation analysis identified and visualized four knowledge domains as the theoretical underpinning of existing smart tourism literature, thereby supplementing, and complementing the findings of the thematic analysis. The constituents of each theme and knowledge domain were discussed to reveal critical gaps and suggest future research directions. [ABSTRACT FROM AUTHOR]

In recent years, the number of publications on social media has increased exponentially. Although several attempts have been made to review social media studies in tourism and hospitality, the social structure of collaborative networks has yet to be analysed. By performing authorship and co-authorship analyses of 385 articles, this study explored the progress of social media research and analysed key authors' productivity, educational background and centrality in research communities. Regional differences in socially constructed social media knowledge were investigated. Networks between universities were also analysed and compared based on region. Findings suggest future research directions for various regions and provide important implications to the academia and industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)