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In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to compare human and machine analyses, highlighting the challenges and opportunities of both approaches.

This study applied qualitative secondary analysis (QSA) with machine learning-based text mining on qualitative data from 25 interviews previously analyzed with traditional qualitative content analysis.

By analyzing both techniques' strengths and weaknesses, this study complements the results from the original research work. The previous human model failed to point to a particular aspect of the case, while the machine analysis did not recognize the sequence of time in the interviewee's discourse.

This study demonstrates that combining content analysis with text mining techniques improves the quality of the research output. Researchers may, therefore, better handle biases from humans and machines in traditional qualitative and quantitative research.

In accounting and finance, researchers have used many ways to detect manager’s fraud risk. Until now, many researchers have used some data mining methods in these two fields to detect this risk. The purpose of this paper is to compare the precision of two data mining methods in detecting such a risk.

For this purpose, this paper analyzed the texts of board’s reports and used two methods including the convex optimization (CVX) method and least absolute shrinkage and selection operator (LASSO) regression method. In this way, the words of these reports, which have the greatest power in explaining the manager’s high fraud risk index, were identified. Using these words, this paper presented a model that could detect manager’s high fraud risk index in companies.

The results indicated that both methods can detect the manager’s high fraud risk index with a precision between 82.55 and 91.25 percent. The LASSO method was significantly more precise than the CVX method.

The lack of access to an official and reliable list of firms suspected to fraud and the lack of access to the Microsoft Word (MS Word) file of board’s reports were two of the most important limitations of this study.

Regulatory bodies and independent auditors can consider the proposed methods in this study for assessing the fraud risk for a firm or other legal parties.

This paper avoided using merely financial statements data to detect the manager’s fraud risk index and focused on texts of board’s reports for the detection process. The capabilities of data mining and text mining methods for detecting the manager’s fraud risk index using board’s reports were tested in this paper. By comparing CVX and LASSO results, this paper indicated that methods with a binary-dependent variable have more power and are more precise than methods with continuous-dependent variables for detecting fraud.

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.

This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance.

This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted.

This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided.

This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons.

This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services.

The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management.

This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.

The present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.

The present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.

The findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”

The originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

Integrating sustainability strategies with business processes is the most challenging task for industry professionals due to the lack of a proper understanding of sustainability concepts. At the same time, a lack of proper guidance restricts them from pursuing such activities. As far as the aspects of implementation are concerned, it is very tough to analyse and pick up key points to start with. The purpose of this paper is to utilize a text mining approach to analyse qualitative data and identify the critical issues for implementing sustainability in the manufacturing sector by focussing on turning processes based on the survey responses of researchers and industry professionals.

An integrated method employing principal component analysis (PCA) and the k-means clustering algorithm has been applied to extract useful information from a set of various suggestions provided by both the groups surveyed. The textual data has also been visualized using word clouds and, thus, it has been compared with the results of the text mining approach.

The results of the study indicate the importance of the role of government organizations and the need for a skilled workforce, which are crucial for enhancing aspects of sustainability in the manufacturing sector, as supported by both researchers and industry professionals. Besides this, researchers have highlighted the need to focus more on environmentally related issues, whereas industry professionals have raised performance-related issues.

The findings of the study present the important concerns of both the groups towards sustainability initiatives and, thus, will help to enhance the understanding of the underlying possibilities of negotiating jointly to enhance the performance of machining processes.

The novelty of this paper lies in its identification of important initiatives that are having a direct impact on the sustainable aspects of the machining process, based on the views of researchers and industry professionals.

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.

This study aims to analyze four text-mining studies of quality management (QM) to illustrate and problematize how the research on quality has informed the quality paradigm since the 1980s. By understanding history, one can better manage current developments.

The findings are based on a meta-analysis of four text-mining studies that explore and describe 11,579 research entries on quality between 1980 and 2017.

The findings show that the research on quality during the past 30 years form a research paradigm consisting of three operational paradigms: an operative paradigm of backend quality orbiting around QM, total QM (TQM) and service quality; an operative paradigm of middle-way quality, circling around the International Organization for Standardization (ISO), business excellence frameworks (BEFs) and quality awards; and an operative paradigm of frontend quality, revolving around reliability, costs and processes. The operative paradigms are interconnected and complementary; they also show a divide between a general management view of quality and a hands-on engineering view of quality. The findings indicate that the research on quality is a long-lived standalone paradigm, supporting the notion of quality being a genuine academic entity, not a fashion or fad.

The empirical basis of the study is four text-mining studies. Consequently, the results and findings are based on a limited number of findings.

Text-mining studies targeting research on quality are scarce, and there seem to be no prior models that depict the quality paradigm based on such studies. The perspectives presented here will advance the existing paradigmatic discourse. The new viewpoints aim to facilitate and deepen the discussion on current and future directions of the paradigm.

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.

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.