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The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

Early childhood and early elementary are key times when children develop internal and external antifat attitudes; thus, it is necessary to better understand the available children’s literature around fatness.This paper aims to examine children's picture books with fat protagonists to better understand the current landscape of children's literature. Drawing on relevant literature around fat characters and the fat studies movement, this critical content analysis considers five children’s books featuring fat protagonists.

This study uses critical content analysis to analyze texts featuring fat protagonists, including two rounds of initial reading and analysis. Using lenses of critical literacy and critical multicultural analysis, the author looks for common themes, silences and absences in the texts, images and peritext.

This paper identifies themes of characters initially internalizing antifatness, then pushing back against antifat bias toward existing with joy and without stigma. Several of these texts even draw on the history of fat activism, highlighting societal critique and a potential activist component of children’s literature with fat protagonists.

The study has a small number of books, due to the limited number of texts that fit the study parameters.

The paper concludes with examples of scaffolding for teachers and parents to have conversations with young children about antifat bias while also acknowledging notable absences, particularly boy protagonists.

These themes illustrate the power of young children to push back against antifat bias and critique oppressive social structures.

There have been very few studies looking at antifatness in children’s picture books. With more books with fat protagonists coming out in the 2020s, this study offers an understanding of the themes present, while also emphasizing the need for an intersectional approach to literature with fat protagonists.

This article aims to explore how the mapping strategies between user requirements expressed by the humanities researchers lead to a better customization of user-driven digital humanities tools and to the creation of innovative functionalities, which can directly affect the way of doing research in a digital context.

It describes the user-driven development of a tool that helps researchers in the quantitative and qualitative analysis of large textbook collections.

This article presents an exemplary user journey map, which shows the different steps of the digital transformation process and how the humanities researchers are involved for (1) producing innovative research solutions, comprehensive and personalized reports, and (2) customizing access to content data used for the analysis of digital documents. The article is based on a case study on a German textbooks collection and content analysis functionalities.

The focus of this article is the reiterative research process, in which humanists (from the human centred point of view) starts from an initial research question, using quantitative and qualitative data and develops both the research question and the answers to it by with the aim to find patterns in the content and structure of educational media. Thus, from the viewpoint of digital transformation the humanist is part of the interaction between digitization and digitalization processes, where he/she uses digital data, metadata, reports and findings created and supported by the digital tools for research analysis.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Purpose: Students’ transition from education to employment is influenced by factors like the length and calibre of their education, demography, labour market conditions, and the general state of the economy. Regardless of the economy, education systems should seek to ensure that students have the skills required for the labour market. This will help them better transition from school to work. This study examines the work skills that companies require for entry-level positions in Romania.

Need for Study: Previously, text analysis studies treated the job market only for the IT industry in Romania. To understand the demand-side opportunities and restrictions, assessing the employment opportunities for young people in the Romanian labour market is necessary.

Methodology: A text mining approach from 842 unstructured data of the existing job positions in October 2022 for fresh graduates or students is used in this chapter. The study uses data from LinkedIn job descriptions in the Romanian job market. The methodology involved is focused on text retrieval, text-pre-processing, word cloud analysis, network analysis, and topic modelling.

Findings: The empirical findings revealed that the most common words in job descriptions are experience, team, work, skills, development, knowledge, support, data, business, and software. The correlation network revealed that the most correlated pairs of words are gender–sexual–race–religion–origin–diversity–age–identity–orientation–colour–equal–marital.

Practical Implications: This study looked at the job market and used text analytics to extract a space of skill and qualification dimensions from job announcements relevant to the Romanian employment market instead of depending on subjective knowledge.

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.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

The purpose of the paper is to present an integrated methodology for identifying trends in a particular subject area based on a combination of advanced text mining and expert methods. The authors aim to test it in an area of clinical psychology and psychotherapy in 2010–2019.

The authors demonstrate the way of applying text-mining and the Word2Vec model to identify hot topics (HT) and emerging trends (ET) in clinical psychology and psychotherapy. The analysis of 11.3 million scientific publications in the Microsoft Academic Graph database revealed the most rapidly growing clinical psychology and psychotherapy terms – those with the largest increase in the number of publications reflecting real or potential trends.

The proposed approach allows one to identify HT and ET for the six thematic clusters related to mental disorders, symptoms, pharmacology, psychotherapy, treatment techniques and important psychological skills.

The developed methodology allows one to see the broad picture of the most dynamic research areas in the field of clinical psychology and psychotherapy in 2010–2019. For clinicians, who are often overwhelmed by practical work, this map of the current research can help identify the areas worthy of further attention to improve the effectiveness of their clinical work. This methodology might be applied for the identification of trends in any other subject area by taking into account its specificity.

The paper demonstrates the value of the advanced text-mining approach for understanding trends in a subject area. To the best of the authors’ knowledge, for the first time, text-mining and the Word2Vec model have been applied to identifying trends in the field of clinical psychology and psychotherapy.

In this paper, the authors review the literature on the nature of the small and medium-sized enterprise concept. The review examines the broad diversity of terms and definitions used to describe these kinds of firms in scholarly and practical settings. They relate this examination to the concept of small business for the purpose of comparison, in order to highlight differences and similarities between the concepts.

Relevant literature including articles from academia and defining documents from practical settings was identified through a scope literature review. Field data were subsequently collected via questionnaires sent to editors and authors of articles related to the theme. The data were content analyzed and the resulting codes consolidated into dimensions in accordance with the Gioia method. Chi-squared tests were applied to categorical data.

The use of the composite category “small and medium” was found to be predominant in the labeling of small businesses in scientific articles, including those in journals that specialize in small businesses, with no justifications presented for this, characterizing a widespread and consensual practice between authors and editors. In the defining documents of practical settings, however, the authors observed greater consistency and precision both in the terms used and in the delimiting values for a small business (self-employed, micro business, small business). In the sample of 27 defining documents mentioned in the articles, 25 specifically defined “small business” and 20 defined “micro business,” using indicators such as number of employees and annual turnover. The indicators delimiting values regarding the category of micro business were the same in all the documents analyzed and, regarding the category of small business, many documents used the same delimiting values.

Recognizing the “non-large enterprise” myth will provide a more effective posture for editors and authors to avoid using the term “small and medium,” resulting in greater precision, understanding and knowledge regarding small businesses. A better definition of a small business by academia can help public policymakers and managers of organizations that support small businesses to tailor their actions better according to the different sizes of companies. This will also lead to social gains, given the importance of small businesses in terms of job creation and countries' economies.

The authors identified and described the myth of the “non-large enterprise” among academics, characterized by the dichotomous view of the business universe, composed of “large enterprises” and “non-large enterprises,” the latter group being characterized by the widespread use of the term “small and medium.”