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The COVID-19 epidemic has brought attention to the variables that influence the mental health of health workers who are entrusted with nursing individuals. Despite the fact that many articles have examined the effects of social media usage on mental health, there is a lack of research synthesizing learning from this body of research. The purpose of this study is to use text mining and citation-based bibliometric analysis to conduct a detailed review of extant literature on health workers’ mental health and social networking habits.

This study conducts a full-text analysis of 36 articles selected on health workers' mental health and social media using text-mining techniques in R programming and a bibliometric citation analysis of 183 papers from the Scopus database in VOS viewer software. But the limitations of the methods used in this study are that the bibliometric analysis was limited to the Scopus database because the VOS viewer program did not support any other database and the text-mining approach caused the natural processing redundancy.

The bibliometric analysis reveals the thematic networks that exist in the literature of health workers’ mental health and social networking. The findings from text mining identified ten topic models, which helped to find the related papers classified in ten different groups and are provided alongside a summary of the published research and a list of the primary authors with posterior probability through Latent Dirichlet Allocation.

To the best of the authors’ knowledge, this is the first hybrid review, combining text mining and bibliometric review, on health workers’ mental health where social networking plays a moderating role. This paper critically provides an overview of the impact of social networking on health workers' mental health, presents the most important and frequent topics, introduces the scientific visualization of articles published in the Scopus database and suggests further research avenues. These findings are important for academics, health practitioners and medical specialists interested in learning how to better support the mental health of health workers using social media.

Information is power. Cliché, but true. The Internet has simultaneously empowered individuals. Prospects can click, choose and control their online interactions. For organisations, the pendulum has swung in the opposite direction. They face more competition and are overloaded with more information than ever before. Manual tracking and processing of competitor activity are tedious, inaccurate, and rapidly outdated. Technology has created the “problem”, and technology can offer potential solutions. This study explores the use of text mining technology to analyse competitors' online promotional text messages. To examine its potential applications, a text mining analysis is applied to top educational sites in the USA. How their Web content is positioned relative to their competitors is analysed and discussed.

The purpose of this paper is to explore the key success factors behind Bangtan Boys’ (BTS) popularity, and how they can contribute to sustaining it, along with detailed strategies for the success of global pop.

This study adopts a mixed-methods approach that uses text mining and interviews and uses the success of BTS to find the key factors accounting for its sustained popularity. For use in text mining, we collected data related to BTS from social network sites (SNS) and analyzed this data using latent Dirichlet allocation (LDA) topic modeling, term frequency analysis and keyword extraction. In addition, we conducted interviews to explore the key factors accounting for the sustained popularity of BTS.

We found ten key success factors—active global fandom, SNS communication, fans' loyalty, empathy through music, storytelling and world view, performance quality, music video quality, overseas expansion at an early stage, efforts for self-development and teamwork among members— for a global pop group's success and sustained popularity.

This study contributes to the literature by finding key factors for success and sustained popularity of a global group through using a mixed-methods approach.

Our results suggest strategies to sustain the popularity of global groups and its potential to benefit across the entertainment industry.

This study is among the first to comprehensively examine the key factors for Korean pop’s (K-pop) sustained popularity by using a mixed-methods approach of text mining and interviews.

The purpose of this study is to compare the trend of academic project management research in Iran and the World in five-year periods with a text mining approach and TF–IDF method.

The research population consists of 1205 theses presented between 2000 and 2019 in Iranian universities. The central library website of the mentioned universities was used for data collection, and the text mining approach with the TF–IDF method was used for data analysis.

The remarkable results of this study include: Concrete structures are the most frequent among structural systems, Risk Management is the most frequent among PMBOK Knowledge Areas, Design-build (DB) system is the most frequent among Project Delivery Systems, Engineering, procurement and construction (EPC) is the most frequent among DB Project Delivery Systems, Financial Management is the most frequent among specialized construction knowledge areas, Soft Skills is the most frequent among Global Trends, Contracting Companies is the most frequent among Project Parties, Construction Projects is the most frequent among Project Areas, Power Plant and Refinery is the most frequent among Project Subjects, Optimization is the most frequent among Problem-Solving Approaches, Fuzzy Logic is the most frequent among Novel Algorithms and Motivation is the most frequent among Soft Skills.

The innovative aspect of this research is that for the first time, text mining has been used to analyze academic research on project and construction management, and also for the first time, academic research on construction industry in Iran has been compared with global research.

In Indonesia, subjective issues towards the fundamental of Islamic banks (IBs) have been arising. For example, they are claimed to be not in line with the Shari‘ah (Islamic law). Furthermore, the existing scholarly works have not much gained knowledge from the local IBs explaining their efforts in promoting maqasid al-Shariah (objectives of Islamic law). Hence, because religiosity drives the fundamental establishment of IBs, this paper aims to explore the knowledge of how IBs in Indonesia promote maqasid al-Shariah via their published reports.

This paper performs text mining from 24 official reports of 5 IBs in Indonesia published from 2015 to 2017. The sample contains 7,162 digital pages and approximately 3,021,618 words. Traditional text mining via human intelligence is first performed to analyse for the numerical data required in the maqasid al-Shariah index (MSI) analysis. Furthermore, a computer-driven text mining using the ‘Text Search’ feature of NVivo 12 Plus is conducted to perform qualitative analysis. These approaches are made to gain relevant knowledge of how the sampled IBs promote maqasid al-Shariah from their published reports.

The analysis using the MSI explains a quantified maqasid al-Shariah on the sample’s performance, which indictes the lowest and the highest performing banks. Furthermore, a qualitative analysis supports the evidence from the quantitative analysis. It explains the authors’ coding process that results in 2 parent nodes and 20 child nodes, which contain 435 references coded from the sampled unstructured and bilingual texts. These nodes explain the information that associates with maqasid al-Shariah from the IBs’ reports. These findings explain how maqasid al-Shariah is measured mathematically and represent relevant knowledge of how maqasid al-Shariah is informed practically via digital texts.

A positivist generalisation is neither intended nor established in this study.

This paper gains relevant knowledge of how the sampled IBs in Indonesia control and maintain the implementation of maqasid al-Shariah from large textual data. Such knowledge is practically important for IBs stakeholders in Indonesia; moreover to help navigate the Shari‘ah identity of Bank Syariah Indonesia (BSI), the new IB established from the merger of 3 state-owned IBs, which are among the sample of this study.

This paper provides evidence that might best challenge the subjective issue of IBs claiming that they are not in line with the Shari‘ah, particularly in Indonesia.

This paper is among the pioneers that discover knowledge of how IBs promote maqasid al-Shariah in Indonesia’s banking sector via a text mining approach.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The Journal of Intellectual Capital (JIC) celebrated its 20th anniversary in 2020. Therefore, the present study aims to provide a general overview of the history and key trends in this journal during 2000–2019.

Two types of citation and textual data during a 20-year journal period were retrieved from the Scopus database. The citation structures and contents were explored based on a combination of bibliometric analysis, altmetric analysis and text mining. The journal themes and trends of their changes were analyzed through citation bursts, mapping and topic modeling. To make a better comparison, the text mining process for the topic modeling of the IC field was performed in addition to the topic modeling of JIC.

Bibliometric analysis indicated that JIC has experienced a remarkable growth in terms of the number of publications and citations over the last 20 years. The results indicated that JIC plays a significant role among IC researchers. Additionally, a large number of researchers, institutes and countries have made contributions to this journal and cited its research papers. Altmetric analysis showed that JIC has been shared in different social media such as Twitter, Facebook, Wikipedia, Mendeley, Citeulike, news and blogs. Text mining abstract of JIC articles indicated that “measurement,” “financial performance” and “IC reporting” have the relative prevalence with increasing trends over the past 20 years. In addition, “research trends” and “national and international studies” had a stable trend with low thematic share.

The findings have important implications for the JIC editorial team in order to make informed decisions about the further development of JIC as well as for IC researchers and practitioners to make more valuable contributions to the journal.

Using bibliometric analysis, altmetric analysis and text mining, this study provided a systematic and comprehensive analysis of JIC. The simultaneous use of these methods provides an interesting, unique and suitable capacity to analyze the journals by considering their various aspects.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

The purpose of this paper is to discuss the current role of qualitative research in the analysis of the relations between brands and consumers in new market spaces, with particular reference to how it can be enhanced with quantitative techniques to study interactions in online communities.

The paper reviews key scientific contributions in the area of qualitative marketing research. Drawing from this theoretical background, the authors then propose the integration of digital ethnography (a qualitative approach) with quantitative text mining as an innovative approach to gain insights into perceptions of brand associations among online consumers.

The paper contributes to a greater awareness of both limitations and new perspectives in relation to qualitative market research, while suggesting innovative paths for future research.

The new methodological approach described can be used to better understand brand knowledge based on consumer brand associations. These insights can then be applied towards developing and implementing effective branding strategies.

The authors propose an interdisciplinary methodology to study consumer behaviour in online communities which incorporates digital ethnography and computer-assisted textual analysis. Particularly the latter technique (borrowed from the field of linguistics) has not yet been exploited extensively in marketing research, but is capable of offering new types of knowledge with important implications for strategic brand management.