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The purpose of this rapid scoping review was to map existing literature on risk communication strategies implemented by authorities and aimed at vulnerable immigrants in the context of pandemics.

Existing literature on the topic was charted in terms of its nature and volume by summarizing evidence regarding the communication strategies. Literature searches were conducted in Academic Search Premier and CINAHL, databases were searched from 2011 to present on March 31, 2021.

Five articles met the criteria and were included in this review, pointing at limited research in this area. The findings indicated that a close interaction between communication authorities and immigrants is important. Community education, building trust in communication sources, clear risk communication and inclusive decision-making among all were found to be important when communicating health risks to immigrants.

The primary limitation of this rapid scoping review is that the literature searches were conducted in only two databases, namely, Academic Search Premier and CINAHL. A wider search across several other databases could have given more profound results. Furthermore, some studies where immigrants were conceptualized as, for instance, “disadvantaged groups” might be overseen due to a choice of the search strategy used in this study. There are also certain limitations related to the studies included in this review.

Identifying efficient ways of conveying recommendations may further assist authorities and scientists in developing more effective health-related risk communication.

This study covered health-related risk communication in the context of pandemics, addressing the need to investigate different groups of immigrants and the challenges related to communicating risks to these groups.

The purpose of this paper is to use the data analysis method of sentiment analysis to improve the understanding of a large data set of employee comments from an annual employee job satisfaction survey of a US hospitality organization.

Sentiment analysis is used to examine the employee comments by identifying meaningful patterns, frequently used words and emotions. The statistical computing language, R, uses the sentiment analysis process to scan each employee survey comment, compare the words with the predefined word dictionary and classify the employee comments into the appropriate emotion category.

Employee responses written in English and in Spanish are compared with significant differences identified between the two groups, triggering further investigation of the Spanish comments. Sentiment analysis was then conducted on the Spanish comments comparing two groups, front-of-house vs back-of-house employees and employees with male supervisors vs female supervisors. Results from the analysis of employee comments written in Spanish point to higher scores for job sadness and anger. The negative comments referred to desires for improved healthcare, requests for increased wages and frustration with difficult supervisor relationships. The findings from this study add to the growing body of literature that has begun to focus on the unique work experiences of Latino employees in the USA.

This is the first study to examine a large unstructured English and Spanish text database from a hospitality organization’s employee job satisfaction surveys using sentiment analysis. Applying this big data analytics process to advance new insights into the human capital aspects of hospitality management is intriguing to many researchers. The results of this study demonstrate an issue that needs to be further investigated particularly considering the hospitality industry’s employee demographics.

This paper aims to investigate the influence of a corporate global mindset and international experience on the internationalization process of Brazilian franchisors. The purpose is to study the role played by global mindset in the internationalization process of franchisors from an emerging country by answering the question: What is the impact of a global mindset on the level of internationalization?

A survey of 104 companies was conducted with franchise managers to compare Brazilian (domestic-only and internationalized) franchisors to foreign franchisors with operations in Brazil. The collected data were analyzed through descriptive statistics, ANOVA and regression analysis.

Results showed that the firm’s experience in international markets (measured as the number of years in foreign countries) has a positive relation to corporate global mindset. Developing a corporate global mindset should help Brazilian franchises to grow internationally. The findings indicate that Brazilian franchisors fall behind their foreign counterparts in international experience, which affect the skills, knowledge, disposition and organizational structure needed to be successful outside their home market.

Franchisors who decide to internationalize should carefully consider the need for developing a global mindset, especially in terms of investing in communication technologies, adequate human resources and an organizational structure to support international operations.

This research contributes to the international franchising literature by exploring the role of a global mindset in the context of franchises from an emerging market. Using a multidimensional concept of corporate global mindset, comprised of global orientation, global knowledge and global skills, this study adds an aspect of international franchising beyond institutional and economic explanations.

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。