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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.

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。

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.

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 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.

This study aims to propose a model to explain online review helpfulness grounded on both previously identified constructs (e.g. review length) and new ones, which have been analyzed in other online reviews’ contexts but not to explain helpfulness.

A total of 112,856 reviews published in TripAdvisor about 21 Las Vegas hotels were collected and a random forest model was trained to assess if a review has received a helpful vote or not.

After confirming the validity of the proposed model, each of the constructs was evaluated to assess its contribution to explaining helpfulness. Specifically, a newly proposed construct, the response lag of the manager’s replies to reviews, was among the most relevant constructs.

The achieved results suggest that hoteliers should invest not only in responding to the most interesting reviews from the hotel’s perspective but also that they should do it quickly to increase the likeliness of the review being considered helpful to others.

论：项解释酒店业中在线评论有用性的模型

研究目的：本论文提出一项模型, 用于解释在线网络的有用性, 这项模型基于之前文献定义的结构（如评论长短）以及在其他在线评论题材下分析的新结构。而这些结构之前未用来解释有用性。

研究设计/方法/途径：样本数据为112,856条TripAdvisor关于21家拉斯维加斯酒店的评论。本论文创建了一种随机森林模型, 用于检测是否一条评论是有用的。

研究结果：本论文肯定了提出模型的有效性, 此外, 本论文评估了每个模型结构, 检测其是否解释了有用性。具体地说, 本论文提出了一种新的结构, 经理回复评论的时间长短, 是最相关的结构之一。

研究原创性/价值：研究结果表明, 酒店经理应该不仅从酒店角度出发回复最有意思的评论, 而且要快速回复, 以增加其他用户评价这条评论是否对他人决策的有用性。

Wine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness.

The study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020.

The authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction.

The limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires.

The proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas.

The proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas.

The methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.

Toward achieving a better guest experience, the current study aims to use the word frequency comparison technique to evaluate the types of attributes and services that are used most frequently in guests’ five- and one-star reviews on TripAdvisor. The working-paper also aims to investigate the differences between reviews written by men and women.

A combined sentiment and text analysis was applied to 329,849 UK hotel reviews from UK TripAdvisor to identify factors that influence customer satisfaction, including those with gender differences.

The present findings reveal important differences between the male- and female-produced terms. The results show that female travelers pay more attention to the hotel’s core products and their comfort compared to male travelers. In terms of food and beverage, men’s comments tended to focus on pubs, beer and certain types of food. In contrast, women’s comments were more likely to be related to healthy eating, such as homemade, vegan and vegetarian foods, as well as fruits and healthy breakfasts. Women also pay more attention to the soft skills of staff such as friendliness, helpfulness and welcoming messages.

While core attributes of a hotel stay remain crucial for all guests, disparities exist between the language men and women use to describe them. For core products, women pay more attention to the room’s cleanliness, comfort and features such as bed, pillow, blanket, towel, toiletries and decoration, whereas men pay more attention to the layout, size and type of room. Hotels may use gender as a segmentation variable and use these findings in their marketing campaigns.

This is one of the first studies offering insights into the differences between the male and female reactions to and preferences for hotel services at a national level. Following a novel method, this study has listed and ranked attributes and differentiated them based on gender.

Online travel reviews platforms have become innovative information systems due to the incorporation of sophisticated gamification elements such as visually appealing badges. This study aims to analyze three features of the review after leveling up a badge: review length (number of words), sentiment scoring and period between two successive reviews (number of days until the next review is written).

A total of 77,000 online TripAdvisor reviews written by 100 frequent travelers and contributors are analyzed using a data mining approach. A data-based sensitivity analysis is then conducted to provide an understanding of the data mining trained models.

The results show evidence that badges appealing for self-pride (“badge passport”) and for peer-recognition (“badge helpful”) have significant influence across the lifespan of online review, whereas badges simply awarded by counting the contributions have little effect.

To the best of the authors’ knowledge, this study provides the first analysis of how an experienced traveler is influenced as the badges and points are being awarded. Intrinsic motivational factor to award badges for standard contributions scarcely influence user behavior. Badges need to be designed to reward accomplishments that are not so trivial to be achieved and that do not depend entirely on the user.

在线旅游评论平台已成为创新的信息系统, 这也是由于结合了复杂的游戏化元素, 例如具有视觉吸引力的徽章。本研究旨在分析升级徽章后评论的三个特征：评论长度（字数）、情绪评分和两次连续评论之间的时间段（距离撰写下一次评论的天数）。

本研究使用数据挖掘方法分析了由 100 位常旅客和贡献者撰写的总共 77,000 条在线 TripAdvisor 评论。然后进行基于数据的敏感性分析 (DSA), 以提供对数据挖掘训练模型的理解。

结果表明, 具有自我自豪感（“徽章护照”）和同行认可（“徽章有帮助”）的徽章在在线评论的整个生命周期中具有显着影响, 而仅通过计算贡献来授予徽章几乎没有影响。

本研究首次分析了经验丰富的旅行者在获得徽章和积分时受到的影响。奖励标准贡献徽章的内在动机因素几乎不会影响用户行为。需要设计徽章来奖励那些并非微不足道且不完全取决于用户的成就。

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.