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Millions of ratings and reviews about products are available on the Internet for free, and they are used by academic researchers in the tourism sector. Data from websites like TripAdvisor are replacing or complementing traditional questionnaires and interviews. The authors are proposing a methodology to estimate the percentage accounted for by the sample of self-interviewed individuals over the total study population, in order to calculate the reliability of the results obtained. Average percentages obtained for hotels cannot be easily generalized due to the high dispersion in participation rates among hotels, even in the same city. Participation levels for tourist attractions are substantially lower than those for hotels and are likely biased, due to the fact that some tourists evaluate places without actually visiting them, merely after viewing them from the outside.

This study aims to contribute perspective into how user-generated content (UGC) can be found to reinforce, or challenge, the commercial promotion of volunteer tourism in Nepal. Volunteer tourism is an expanding alternative niche market spanning the globe. More understanding is needed to identify potential conflicts that may arise in relation to information communicated via official promotional materials from volunteer tourism organisation websites and from UGC on sites such as TripAdvisor.

A qualitative content analysis of websites and online UGC gathered from identified promotional websites and TripAdvisor used to inform and persuade tourists was analysed.

Three themes emerged from the analysis of promotional websites: perspective outcomes of volunteerism on locals and VolunTourists, requirements of participating volunteers and the ability of volunteer organisations to effectively meet expectations. The TripAdvisor forum content is broken down into two main areas: requests/recommendations and critical reflections. UGC is playing a pivotal role to inform future VolunTourists, based on experiences by previous volunteers. Commercial websites emphasise achievable positive outcomes by unskilled/unqualified volunteers, with the support of credible volunteer organisations. Conversely, UGC focusses on negative outcomes associated with volunteer tourism.

This study provides insights into how online platforms are being used by consumers. This study suggests that significant disparities exist between content communicated via promotional websites and UGC, with a number of important implications discussed.

This study aims to address this gap in the understanding of the authors through the analysis of emerged themes communicated via Nepal volunteer websites and TripAdvisor forums. Moreover, there is a need for volunteers to be more informed about the complexities of the industry and volunteer organisations, acknowledging more needs to be considered to meet the requirements/expectations of volunteers.

The purpose of this paper is to examine the significance of TripAdvisor on reputation within the hotel industry. TripAdvisor encapsulates key themes in establishing an online reputation strategy in an evolving digital landscape.

Through the use of an exploratory case study, data were gathered primarily by means of a series of expert interviews within the hotel industry in Cyprus, today a mature holiday destination in Europe. Further data collection included a document search of presentations, annual reports, past surveys and sales and marketing literature from the examined industry.

Hotel communication practitioners are fully aware of the impact of social media in managing reputation. Constant monitoring, prompt responses, training and transparency were identified as key factors. Online reputation management needs to be taken into consideration when designing a comprehensive integrated communication strategy.

Congruence amongst interviewees in certain areas could be on account of the homogeneity of practitioners, of their background and training and of similar organisational cultures across the locale of study. This leads to limits in the generalisations from this study’s findings.

Encouragement and training of employees were amongst the primary suggestions that emerged. An internal and external environmental scan, recognising possible strengths, weaknesses, opportunities and threats, which could assist in the effective engagement and monitoring of the organisation’s online presence, were also suggested.

The uniqueness of the study lies in its exploration of reputation management of a well-known traveller’s platform by addressing social media content in both a proactive and reactive manner.

The purposes of this study are to identify how hotel guests perceive green practices and to explore how hotels effectively inform customers of their green practices through social media such as TripAdvisor.

To examine hotel guests’ awareness of green practices through social media, this research investigated guests’ comments about green practices and management responses on TripAdvisor using content analysis.

The results indicated that most guests respond positively toward green practices when they can recognize them, e.g. reducing energy usage or water saving. However, lack of awareness about hotels’ green practices can cause guests to feel inconvenienced during their stays. Moreover, the study found that only a few hotel managements provide feedback on guests’ negative comments on TripAdvisor to inform them about the hotels’ green practices.

This research is limited to analyzing only the top ten green hotels in the USA ranked by TripAdvisor. A study of more hotel cases with green practice standards, which could be adjusted to involve the use of different service levels such as luxury, upscale or economy hotels, may provide more insights into this discussion.

This research presents an exploratory intent to probe guests’ comments and management responses about green practices in the US lodging industry. The results provide empirical evidence of hotel guests’ perceptions of green practices as posted on social media. Moreover, management can use social media feedback as an educational tool and as effective advertisement, which in turn may reduce the negative perception of hotel green programs.

To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

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

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。

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。

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。

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。

This paper aims to examine when travelers are more influenced by friends (word-of-mouth [WOM]) with limited knowledge of hotels but an understanding of the traveler, and when by review sites (electronic word-of-mouth [eWOM]) which have immense experience of hotels but cannot know the individual traveler. Sites such as TripAdvisor® offer millions of reviews, and travelers often reduce that to a manageable amount by focusing on reviews by writers who show homophily, i.e. are similar to them in terms of travel interests. These sites represent a form of eWOM recommendation; what is not clear is how much they replace or augment traditional WOM.

Dual-method content analysis of semi-structured interviews with a heterogeneous purposive sample of regular users of TripAdvisor (N = 30), followed by a survey of TripAdvisor users (N = 237).

Friends were considered the most credible information source, although friends showing greater homophily were more valued than others. However, in some circumstances, subjects found eWOM more credible: when they wanted greater certainty in their hotel choice, so complete information was important; when the hotel was for a special occasion or special people; and for feelings of empowerment. Most subjects compared all sources rather than relying on one.

This study reminds hotel managers that while eWOM is accessible and analyzable, it may not fully represent guests’ opinions; hotels’ marketing strategy should balance it with other recommendation networks. As guests compare sources, consistency in all forms of customer engagement is also essential.

The purpose of this paper is to examine guests’ experiences at green hotels and the impact of green experience on customer satisfaction.

A total of 727 green reviews (reviews on green experiences) of the top ten green hotels in the USA were downloaded from TripAdvisor for content analysis. Descriptive statistics and ordinal logistic regressions were then used.

Guests have both positive and negative experiences at green hotels. “Energy”, “purchasing” and “education and innovation” are the most frequently discussed green practices. Some guests’ green experiences, such as “guest training”, “energy”, “water”, “purchasing” and “education and innovation”, significantly influence their overall satisfaction with hotels. Compared with basic green practices, advanced green practices tend to have greater impacts on customer satisfaction.

This study provides insight into guests’ green experiences at hotels and their impact on customer satisfaction. More importantly, this study examines the contribution of different types of green practices to customer satisfaction. As the green hotels examined in this study were not randomly selected, the results should be interpreted with caution.

Different practices impact customer satisfaction in different ways, so hoteliers should refine their green strategies when they implement these green practices.

Very few studies have examined the relationship between green practices and customer satisfaction. A gap still exists in specifically what types of green practices affect customer satisfaction and whether different levels of green practices have different impacts on customer satisfaction. This study investigates guests’ actual experiences and fills the above research gap.

Recent figures show that users are discontinuing their usage of TripAdvisor, the leading user-generated content (UGC) platform in the tourism sector. Hence, it is relevant to study the factors that influence travelers’ continued use of TripAdvisor.

The authors have integrated constructs from the technology acceptance model, information systems (IS) continuance model and electronic word of mouth literature. They used PLS-SEM (smartPLS V.3.2.8) to test the hypotheses using data from 297 users of TripAdvisor recruited through Prolific.

Findings reveal that perceived ease of use, online consumer review (OCR) credibility and OCR usefulness have a positive impact on customer satisfaction, which ultimately leads to continuance intention of UGC platforms. Customer satisfaction mediates the effect of the independent variables on continuance intention.

Managers of UGC platforms (i.e. TripAdvisor) can benefit from the findings of this study. Specifically, they should improve the ease of use of their platforms by facilitating travelers’ information searches. Moreover, they should use signals to make credible and helpful content stand out from the crowd of reviews.

This is the first study that adopts the IS continuance model in the travel and tourism literature to research the factors influencing consumers’ continued use of travel-based UGC platforms. Moreover, the authors have extended this model by including new constructs that are particularly relevant to UGC platforms, such as performance heuristics and OCR credibility.

Building on a TripAdvisor data for five Mediterranean destinations, namely, Greece, Croatia, Italy, France and Spain, this study analyses the constituents of restaurants' online reputation and their interrelation with destination competitiveness, in particular two Travel and Tourism Competitiveness Index (TTCI) pillars, namely, Prioritisation of Travel and Tourism and Price Competitiveness.

The analysis has revealed that restaurants' online reputation is positively influenced by two factors, namely, Core elements, i.e. cooking, service and price-quality ratio, and Price. Furthermore, the restaurants' online reputation does not influence destination competitiveness (TTCI) directly, but indirectly throughout its main constituents, i.e. service and price. Price is the only variable with significant influence on overall TTCI. Within the sample of these destinations, Balkan countries, i.e. Greece and Croatia, perform very well in terms of their restaurants' online reputation. On the other hand, considering the overall TTCI rating, their competitive positions are substantially lower than those of Italy, France and Spain.

The study provides new insights into the relationship between gastronomic offer and destination competitiveness, and valuable practical implications for destination and hospitality management. Moreover, this study addresses various gaps in existing research on this topic. Specifically, it validates the reputation elements presented online using TripAdvisor data and analyses the impact of electronic Word of Mouth (eWOM) not only as the outcome variable of other constructs, as is the case in the literature, but also as a central construct of the analysis. In doing so, it extends current research on this topic and fills the gap regarding the inclusion of the supply-side stakeholder perspective, which has long been recognised as necessary in any attempts to measure competitiveness.