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Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

Extant research shows mixed results on the impact of expressed negative emotions on donations in online charitable crowdfunding. This study solves the puzzle by examining how different types of negative emotions (i.e. sadness, anxiety and fear) expressed in crowdfunding project descriptions affect donations.

Data on 15,653 projects across four categories (medical assistance, education assistance, disaster assistance and poverty assistance) from September 2013 to May 2019 come from a leading online crowdfunding platform in China. Text analysis and regression models serve to test the hypotheses.

In the medical assistance category, the expression of sadness has an inverted U-shaped effect on donations, while the expression of anxiety has a negative effect. An appropriate number of sadness words is helpful but should not exceed five times. In the education assistance and disaster assistance categories, the expression of sadness has a positive effect on donations, but disclosure of anxiety and fear has no influence on donations. Expressions of sadness, anxiety and fear have no impact on donations in the poverty assistance category.

This work has important implications for fundraisers on how to regulate the fundraisers' expressions of negative emotions in a project's description to attract donations. These insights are also relevant for online crowdfunding platforms.

Online crowdfunding research often studies negative emotions as a whole and does not differentiate project types. The current work contributes by empirically testing the impact of three types of negative emotions on donations across four major online crowdfunding categories.

Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour.

More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis.

The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour.

This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.

Readers articulate mood in deeply subjective ways, yet the underlying structure of users' understanding of the media they consume has important implications for retrieval and access. User articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable potential to provide a better searching experience for all involved. The current study develops mood categories inductively for fiction organization and retrieval in information systems.

The authors developed and distributed an open-ended survey to 76 fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction reader responses, the research team identified 161 mood terms and used them for further categorization.

The inductive approach resulted in 30 categories, including angry, cozy, dark and nostalgic. Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies.

The inherent complexity of “mood” should not dissuade researchers from carefully investigating users' preferences in this regard. Adding to the existing efforts of classifying moods conducted by experts, the current study presents mood terms provided by actual end-users when describing different moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as well as structures derived from user-provided terms that ultimately have the potential to improve user experience.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

Space tourism is fairly neglected in academic research and requires further exploration. Public reaction on social media offers great insights to understand the patterns of behaviour but is often ignored as a potential data source. Thus, this study aims to fill the gap by add to the literature on space tourism, social media analytics and behaviour.

The study adopts a qualitative approach and uses Twitter data for drawing conclusions. An exploratory design is used by analysing 10,000 tweets through unsupervised machine learning and two sets of analysis were conducted. First, sentiment analysis is performed using NRC Emotion Lexicon, which classifies the data as per eight basic emotions and polarity as positive and negative. The findings are complemented with a comparison cloud. Second, LDA Topic modelling using Gibbs Method is used to find ten broad topics that are used for discussions in space tourism tweets. Data visualisation technique is used to depict results using R language on RStudio.

A total of 21,784 emotions have tapped using the NRC Emotion Lexicon. Results indicate the dominance of positive sentiments (25%) with it surpassing the negative sentiments by many folds. The top emotions include trust and anticipation. The LDA-based Topic modelling identified seven correlated topic models that have been grouped by the author as space tourism in media, aspirations, ethical issues, criticism, descriptive, symbolism and miscellaneous.

To the best of the author’s knowledge, no study has attempted to study the response of space tourism on social media by tapping discussions in the form of Tweets. Thus, this study adds extensively and acts as a preliminary investigation on the public sentiments of space tourism on social media.

Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.

A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.

The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.

The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

This study aims to explore the emotional landscapes and spatial preferences of digital nomads, focusing on how sentiments expressed in destination reviews influence their mobility and destination choices.

Employing a lexicon-based sentiment analysis of social media comments and reviews, alongside advanced geographical information systems (GIS) mapping techniques, the study analyzes the emotional tones that digital nomads associate with various destinations worldwide.

The analysis reveals significant patterns of emotional sentiments, with trust and joy being predominant in preferred destinations. Spatial patterns identified through GIS mapping highlight the global distribution of these sentiments, underscoring the importance of emotional well-being in destination choice.

Insights from this study offer valuable guidance for Destination Management Organizations (DMOs) in strategic planning, enhancing destination appeal through targeted marketing strategies that resonate with the emotional preferences of digital nomads.

This research introduces a novel approach by integrating sentiment analysis with GIS to map the emotional and spatial dynamics of digital nomadism, contributing a new perspective to the literature on tourism and mobility.