The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021 to 2022 published on The Guardian website, a key global mainstream media outlet. To perform thematic content analysis of the qualitative data, this research used the Leximancer software, and the The Natural Language Toolkit (NLTK) from NLP libraries were used to identify sentiment. Further, an AI-based Monkeylearn API was used to make sectoral classifications of the main topics that emerged in the Leximancer analysis. The key themes which emerged in the Leximancer analysis, included "metaverse", "Facebook", "games" and "platforms". The sentiment analysis revealed that of all articles published in the period of 2021–2022 about the metaverse, 61% (n = 622) were positive, 30% (n = 311) were negative, and 9% (n = 90) were neutral. Positive discourses about the metaverse were found to concern key innovations that the virtual experiences brought to users and companies with the support of the technological infrastructure of blockchain, algorithms, NFTs, led by the gaming world. Negative discourse was found to evidence various problems (misinformation, harmful content, algorithms, data, and equipment) that occur during the use of Facebook and other social media platforms, and that individuals encountered harm in the metaverse or that the metaverse produces new problems. Monkeylearn findings revealed “marketing/advertising/PR” role, “Recreational” business, “Science & Technology” events as the key content topics. This study’s contribution is twofold: first, it showcases a novel way to triangulate qualitative data analysis of large unstructured textual data as a method in exploring the metaverse concept; and second, the study reveals the characteristics of the metaverse as a concept, as well as its association with other related concepts. Given that the topic of the metaverse is new, this is the first study, to our knowledge, to do both.

This study examines the relationship among work engagement, psychological empowerment, and perceived organizational support in fostering innovative work behavior (IWB) within sustainability-oriented organizations. Using latent class analysis (LCA), employees were classified into distinct profiles based on their levels of engagement, empowerment, and perceived support. The findings indicate that employees with higher engagement and organizational support exhibit significantly greater innovative work behavior, while those with lower psychological and organizational resources face challenges in contributing to innovation. This study also reveals that sustainability practices enhance the positive impact of these resources on innovation, improving both employee well-being and organizational performance. Grounded in Structural–Interactionist Innovation Theory, Self-Efficacy Theory, and Social Exchange Theory, this research provides practical insights for organizations to develop targeted strategies, such as tailored training programs and leadership support, to promote innovation and sustainability.

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information.

Online communities such as Reddit offer neurodivergent individuals a unique space to express emotions, seek psychosocial support, and negotiate identity outside conventional social constraints. Understanding how these communities articulate and structure emotional discourse is essential for inclusive technology design. This study employed a hybrid natural language processing (NLP) framework that integrates lexicon-based sentiment analysis (VADER) with transformer-based topic modeling (BERTopic). A dataset comprising 238,100 posts and comments from subreddits focused on autism, ADHD, neurodiversity, and mental health (January 2023-February 2024) was analyzed to detect thematic and affective patterns in digital neurodivergent discourse. The analysis revealed ten semantically coherent and emotionally salient topics, including emotional overload, masking, diagnostic barriers, coping strategies, and AI-mediated interaction. Sentiment-topic alignment highlighted how affective tone is embedded within distinct thematic clusters, underscoring the structured emotional dynamics of neurodivergent communities. This study advances methodological rigor in affective computing and human computer interaction by combining scalable NLP tools with visual analytics to map digital neurodivergent discourse. It contributes a reproducible framework for analyzing emotion-rich online content and provides empirical grounding for the inclusive design of emotionally responsive digital environments tailored to neurodivergent users.

This study set out to better understand virtual consumerism (VC) by applying natural language processing (NLP) methods for sentiment and content analyses. A total of 318 articles related to VC were identified on theguardian.com Web site and analyzed by text mining methodology. A thematic, content analysis using the Leximancer program was performed to explore VC as a concept, and its related concepts and concept associations. For the purposes of “deep-dive insights,” further content and sentiment analyses were performed with MonkeyLearn and valence aware dictionary for sentiment reasoning. This triangulation in methodology enabled a comprehensive unstructured qualitative data analysis. The study identified key themes that characterize and define VC. It uncovered that, although there is predominantly positive sentiment toward VC reported in The Guardian online articles, negative sentiment also exists, presenting challenges for the industry to maneuver. The findings reveal that in the context of VC, a virtual experience is also a social experience in a virtual space, which is becoming and evolving. There are certain industries and sectors that are embracing VC, such as marketing, advertising and public relations, software development/IT, art/design, and entertainment, as well as science/technology. Some sectors and industries are experiencing challenges, such as security/law enforcement and medical, and hence display negative sentiment toward VC. Overall, this study presents a working definition of VC, a synopsis of the state of VC, and highlights areas for potential research to further our understanding of this phenomenon. It contributes to an improved understanding of VC for the industry and academia, and provides impetus for future studies focused on the emergent VC-relevant conceptual relationships.