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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.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

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.

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.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.