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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。

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

The market segment of families with young children is significant for the cruise industry in China. This study aims to examine the cruise experience of them and facilitate the provision of family cruise vacation products and services.

This study collects online comments on the cruise experience of Chinese families with young children and analyzes the data in terms of text, sentiment and content.

The results of the text analysis indicate issues with several dimensions of the cruise experience. But the results of a sentiment analysis reveal that only 56.76% of the Chinese families with young children expressed positive feelings about cruise tourism. Almost one third (30.43%) expressed negative feelings, and the rest were neutral. In addition, by conducting a content analysis of the negative sentiment comments, this study identifies areas where cruise products and services need to be improved.

Cruise lines are advised to improve the provision of family cruise vacation products and services, such as enhancing physical experience elements, paying attention to human interaction and situational factors and caring for travelers’ personal needs.

This paper explores the cruise experience of Chinese families with young children, which is currently underdeveloped. A methodology approach including text analysis, sentiment analysis and content analysis is systematically applied to offer a holistic and deep understanding of travel experience. A framework for the cruise experience is proposed, which has theoretical and practical significance.

携儿童出游的中国家庭邮轮体验研究

摘要

有小孩的家庭细分市场对中国的邮轮行业来说意义重大。本研究旨在通过分析他们的邮轮体验, 提升家庭邮轮度假产品和服务的开发与运营。

本研究通过收集携儿童出游的中国家庭邮轮体验的相关在线评论, 对其从文本分析、情感分析和内容分析等方面对数据进行了分析。

文本分析的结果表明邮轮体验的若干维度上存在问题。但情感分析的结果显示, 只有56.76%的有小孩的中国家庭对邮轮体验表达出了积极情绪。近三分之一 (30.43%) 表达了负面情绪, 其余的则持中立态度。此外, 通过对负面情绪评论进行内容分析, 本研究提出了邮轮产品和服务需要提升的方向。

建议邮轮公司根据本研究的结果改进家庭邮轮度假产品和服务, 如增强身体体验元素, 关注人际互动和情境因素, 关心旅客的个性化需求等。

本文对目前尚没有进行充分探讨的携儿童出行的中国家庭的邮轮体验进行了分析, 为了更全面深入地理解邮轮旅行体验, 本研究系统地应用了包括文本分析、情感分析和内容分析等方法, 提出了邮轮体验的研究框架, 具有一定的理论和实践意义。

Experiencias vacacionales en crucero para familias chinas con niños pequeños

Resumen

El segmento de mercado de familias con niños pequeños es significativo para la industria de cruceros en China. Este estudio tiene como objetivo examinar la experiencia de crucero de este segmento, así como facilitar la oferta de productos y servicios de vacaciones familiares en crucero.

Este estudio recopila comentarios en línea sobre las experiencias de crucero en las familias chinas con niños pequeños, analizando los datos en términos de texto, sentimiento y contenido.

Los resultados del análisis del texto indican problemas con varias dimensiones de la experiencia del crucero. Pero los resultados del análisis de sentimientos, revela que solo el 56,76% de las familias chinas con niños pequeños expresaron sentimientos positivos sobre el turismo de cruceros. Casi un tercio (30,43%) expresó sentimientos negativos, y el resto fue neutral. Además, al realizar un análisis de contenido de los comentarios de sentimiento negativo, este estudio identifica áreas donde los productos y servicios de cruceros deben mejorarse.

Se recomienda a las líneas de cruceros que mejoren la oferta de productos y servicios vacacionales familiares, así como mejorar los elementos de la experiencia física, prestar atención a la interacción humana y los factores situacionales, y atender las necesidades personales de los viajeros.

Este trabajo explora la experiencia de crucero de las familias chinas con niños pequeños, que actualmente está poco desarrollada. Se aplica sistemáticamente un enfoque metodológico que incluye análisis de texto, análisis de sentimiento y análisis de contenido para ofrecer una comprensión holística y profunda de la experiencia de viaje. Se propone un marco para la experiencia del crucero, que tiene significado teórico y práctico.

The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics.

Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article.

The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding.

This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.

本文对酒店旅游学科中采用社交媒体数据分析的文献进行梳理。本文通过审阅其相关分析方法的文献来提出分析方法的改进策略。

样本数据包括过去18年中五个国际在线文献索引库中的文献。搜索通过标题、关键词、或者摘要中出现社交媒体数据分析等相关字样的文章。书章节、会议文章、以及非英文文章未被收录在索引中。系统回顾和文献综述的方法（PRISMA）指导本文文献索引, Stieglitz和Dang-Xuan（2013）社交媒体数据分析框架作为本文文献分类的方法。

本文汇报了每篇文献的研究目的以及系统归类以更好理解社交媒体数据分析的研究问题以及每种方法的使用频率。自2014年起, 社交媒体数据分析快速增长, 以及其他相关分析方法, 包括精度测试（accuracy testing）。这些结果表明更多全面、稳定的分析方法需求增强以及竞争激烈。本文推荐使用改良方法, 比如社交网络分析法、比较分析、趋势分析等。消费者评价网络和社交网站成为主要社交媒体网络数据的提供平台。本文推荐多源数据应该同步分析以提高有效性和全面性的理解。

本文是首篇酒店旅游领域中对社交媒体数据分析的系统文献回顾型文章。本文强调了社交媒体数据分析的先进性以及扩展其方法的全面性；常见分析方法比如文本分析和情感分析应该结合非常见的分析方法比如比较分析法和空间分析法进行系统分析。

关键词 – 关键词 对比分析, 情感分析, 用户原创内容,社交媒体分析, 主题模型, 空间分析, 文本分析文章类型 文献综述

This study aims to examine technology adoption practices in Chinese theme parks by leveraging text mining and sentiment analysis approaches on actual theme park customers’ online reviews.

The study text mined a total of 65,518 reviews of 490 Chinese theme parks with the aid of the Python program. Further, it computed sentiment scores of the customer reviews associated with the ratings of each categorized technology practice applied in the theme parks.

The study identified two major categories of technology applications in theme parks: supporting and experiential technologies. Multiple statistical tests confirmed that supporting technologies consisted of three types: intelligent services, ticketing and in-park transportation. Experiential technologies further included five aspects of technologies according to Schmitt’s strategic experiential modules (SEMs): sense, feel, act, think and relate.

The study findings contribute to the current understanding of theme park visitors’ perceptions of technology adoption practices and provide insightful implications for theme park practitioners who intend to invest in high technology solutions to deliver a better customer experience.

通过对游客的在线评论进行文本挖掘和情感分析, 本研究论旨在探索在中国主题公园中科技采用的行为。

本研究运用Python 程序一共挖掘了来自490 中国主题公园的65,518 条评论。本研究进一步计算了在主题公园中与科技运用有关在线评论的情感指数。

本研究发现了在主题公园科技应用的两大主要分类：辅助和体验科技。辅助科技包括三种：智慧服务, 售票, 和园中运输。根据Schmitt 战略体验模块（SEMs): 体验科技进一步包括科技的五大方面：感官, 感觉, 思考, 和联系。

本研究对了解目前主题公园游客对科技使用行为的看法提供了见解, 以及对主题公园有意向投资科技来提高客户体验的从业人员提供了深远意义。

The purpose of this study is to determine the satisfaction of the guests who stay at hotels offering technology-supported products and services related to the services and products they receive by using the opinion mining technique.

In this research, 12,396 customer reviews on booking.com related to ten hotels belonging to a hotel chain using technology-supported products were evaluated with aspect-based sentiment analysis techniques.

As a result of this study, it has been determined that using technology in hotel businesses creates a positive impression on customer satisfaction. It has been determined that the enrichment of standard hotel business products such as beds and room lighting with technology, in a way that will not be very costly, affects the guests. In addition, it is interesting that technological features such as robots and room service robots, which are called “High & Technology” in this study, are evaluated by customers in the service process.

The hotel managements have the opportunity to evaluate the services we offer by analyzing their online comments and to see their own image from the eyes of the guests. Hotel businesses must learn about customer expectations for technologies with high investment costs. This study, which analyzes online customer reviews, enables tourism businesses that offer technology-supported products and services and invest in technology in service delivery, to understand how customers evaluate the service.

In this study, customer reviews of a hotel group operating in many countries belonging to a hotel group that enriches its standard products with technology and provides service with the concept of a “smart hotel” were examined. This study contributes to the understanding of customers' experience of using technological products in hotel businesses. This study contributes to the literature on customers' satisfaction with technological hotel products and services and the decision of hotels to invest in technology.

本研究通过使用意见挖掘技术旨在确定入住酒店的客人的满意度。这些客人接受酒店提供的产品相关的技术支持和服务。

在这项研究中, 使用基于特定方面的情感分析技术评估了 booking.com 上与属于连锁酒店的 10 家酒店相关的 12,396 条客户评论, 这些酒店均使用科技支持的产品。

作为这项研究的结果, 已经确定在酒店业务中使用科技会对客户满意度产生积极的影响。已经确定, 标准酒店商务产品（例如床、房间照明等）的丰富科技以不会非常昂贵的方式影响客人。此外, 客户感兴趣的是在本研究的服务过程中包括机器人和技术特征, 例如客房服务机器人, 作为“高科技”层面。

酒店管理层分析了客人的在线评论, 并有机会评估他们的服务并从客人的眼中看到自己的形象。酒店企业必须了解客户对高投资成本技术的期望。本研究分析了直接体验过的消费者的意见。因此, 研究结果将使决定投资服务技术的酒店企业受益。

在这项研究中, 我们对一家在许多国家经营的酒店集团的客户评论进行了调查。该酒店属于一家酒店集团, 该集团以技术丰富其标准产品, 并以“智能酒店”的概念提供服务。该研究有助于了解客户在酒店业务中使用技术产品的体验。该研究扩展了客户对酒店科技产品和服务的满意度的文献, 以及对酒店投资技术的决策做出贡献。

This study aims to quantify the underlying feelings of online reviews and discover the role of seasonality in customer dining experiences.

This study applied sentiment analysis to determine the polarity of a given comment. Furthermore, content analysis was conducted based on the core attributes of the customer dining experiences.

Positive feelings towards the food and the service do not show a linear relationship, while the overall dining experiences increase in line with the positive feelings on food quality. Moreover, feelings towards the atmosphere of the restaurants are the most positive in peak season.

This study provides guidelines for restaurateurs regarding the aspects that need more attention in different seasons.

The paper contributes to the knowledge of customer feelings in local restaurants/gastronomy and the role seasonality plays in fostering such feelings. In addition, the novel methodological procedures provide insights for tourism research in discovering new dimensions in theories based on big data.

本论文旨在量化在线评论中的情感导向以及发掘季节性对消费者用餐体验的作用。

本论文采用情感分析法对既定评论做出情感判断。此外, 本文还依据消费者用餐体验中的核心价值采用了内容分析法。

研究发现消费者对食物和服务的正向情感并不是线性关系。然而, 整体用餐体验与对食物质量的正向情感是线性正向的关系。此外, 消费者对饭店氛围的情感在旺季的时节是最为突出的。

本论文对饭店从业者在不同季节的关注点上起到了指导作用。

本论文对地方饭店/美食的消费者情感认知做出了贡献, 此外, 本论文还对季节性如何促进消费情感的作用做出了研究。本论文还采用了新型的研究方法, 这对于旅游研究来说, 做出了基于大数据的新理论研究方向。

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

This study analyzes the impact of sports social media on a country regarding three international events connected to the brand Spain. It examines (1) the use and importance of various social media platforms in sports events and (2) identifies the countries generating the most social media content.

A total of 1,711,084 posts were collected for analysis, focusing on FIFA World Cup Qatar 2022, XLI Marathon Valencia Trinidad Alfonso 2022 and Davis Cup 2022, with a particular emphasis on the Spain brand. Through Atribus, diverse social media data were recovered and analyzed. Later, we recommended employing various metrics and ANOVAs to address the research questions. Additionally, we conducted a sentiment analysis.

The results show differences between (1) the use and relevance of social network platforms and events and (2) the content generated by different countries. The practical implications offer valuable insights for sports event organizers, destination managers and other stakeholders. The research implications suggest potential avenues for future research based on the observed patterns and behaviors in social media posts related to sports events and Brand Spain.

(1) Some papers have studied the role of sports events’ social media, ignoring the comparison among different social media platforms; (2) usually, previous literature has focused on a single event or sport and (3) although there is considerable research related to the strategic and operational Inés Küster Boluda Inés Küster Boluda role of social media, there is less systematic analysis related to the extent sports events use social media in general and in specific social media platforms and virtually nonexistent studies that employ index measurements.

本研究擬分析就三個與西班牙品牌有關的國際體育賽事而言，體育社交媒體對一個國家的影響。俱體而言，本研究擬探討： (一) 、各體育賽事社交媒體平台的使用和其重要性，以及 (二) 、是哪些國家生成最多的社交媒體內容。

研究人員收集共計1,711,084帖子以便進行分析;其焦點放在2022年卡塔爾世界盃 (即2022年國際足聯世界盃) ，2022年特尼利尼達阿方索馬拉松賽－瓦倫西亞 (XLI Marathon Valencia Trinidad Alfonso 2022) 和2022年台維斯盃;研究人員特別把重點放在西班牙品牌上。研究人員透過 Atribus 重新取得多種多樣的社交媒體數據，然後進行分析。之後，研究人員建議使用不同的度量和方差分析去處理各研究問題，以及進行了情感分析。

研究結果顯示了以下兩者之差異：(一) 、社交網絡平台和比賽項目的使用和關聯，以及 (二) 、不同國家生成的內容。從這個研究發現，體育賽事的籌辦者、目的地管理人員和其它利益相關者，均會獲得寶貴的實務啟示;至於就未來學術研究的路向而言，學者和研究人員或許可觀察關於體育賽事和西班牙品牌的社交媒體帖子裡顯示的模式和行為，從而找到合適的研究路徑。

本研究有以下的貢獻：(一) 、從前的研究多只探討關於體育賽事的社交媒體所扮演的角色，而忽略了要比較不同社交媒體平台的需要;(二) 、過去的文獻通常聚焦於單一的賽事或運動上;以及 (三) 、雖然探討關於社交媒體在戰略上和在操作上所扮演的角色的研究為數不少，但甚少研究、就體育賽事大體使用社交媒體的程度，或使用特定的社交媒體平台的程度進行分析和探討。再者，幾乎沒有學者或研究人員在有關的研究上使用指標測量法;就此三點而言，本研究可說填補了有關的研究空白。

There is a limited understanding of experience revelation in tourism. This study aims to fill the gap by investigating the influence of review platforms’ characteristics, i.e. time-dimension and interactivity, on this issue to generate a holistic view of customer experience.

This study analysed data from Google Reviews, TripAdvisors and Twitter, consisting of 41,914 records within a three-year span, about Komodo National Park, Indonesia. An explanatory sequential mixed method was performed, adopting quantitative sentiment analysis with a naïve algorithm, opinion lexicon and Latent Dirichlet Allocation for topic modelling, followed by a qualitative analysis.

The findings support the proposed interaction between the characteristics of the platforms and the extent of customer experience shared through the platforms. Further elaboration of the data brought up five propositions on the relationship between the time dimension and interactivity characteristics of the review platforms and experience sharing on the platforms.

This study presents an original and initial effort to gather a holistic view on customer experience. It brings valuable implications to the theory and practice of customer experience management, especially in the tourism sector.

目前文献对旅游体验启示的认识有限。 本研究通过调查评论平台的特征（即时间维度和交互性）对此问题的影响来填补空白, 以生成客户体验的整体视图。

本研究分析了来自谷歌评论、TripAdvisors 和 Twitter 的数据, 包括三年内关于印度尼西亚科莫多国家公园的 41,914 条记录。 运用了解释性顺序混合方法, 采用朴素算法、意见词典和隐含狄利克雷分布进行主题建模的定量情感分析, 然后进行定性分析。

调查结果支持所提出的平台特征与通过平台共享的客户体验程度之间的相互作用。 对数据的进一步阐述, 提出了评论平台的时间维度和交互特征与平台经验分享之间关系的五个命题。

本研究通过一项原创和初步的努力收集了关于客户体验的整体观点。 它为客户体验管理的理论和实践带来了宝贵的启示, 尤其是在旅游领域。