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This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

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.

The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly.

By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts.

Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment.

Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP.

To the best of the authors’ knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS.

Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.

This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.

In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.

The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.

This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

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.

Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.

The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.

To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.

Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

This study aims to develop a practical and effective approach for market segmentation using customer experience dimensions derived from online reviews.

The research investigates over 6,500 customer evaluations of food establishments on Taiwan’s Yelp platform through the Latent Dirichlet allocation (LDA) data mining approach. By using the LDA-derived experience dimensions, cluster analysis discloses market segments. Subsequently, sentiment analysis is used to scrutinize the emotional scores of each segment.

Mining online review data helps discern divergent and new customer experience dimensions and sheds light on the divergent preferences among identified customer segments concerning these dimensions. Moreover, the polarity of sentiments expressed by consumers varies across such segments.

Analyzing customer attributes extracted from online reviews for segmentation can enhance comprehension of customers’ needs. Further, using sentiment analysis and attributes of online reviews result in rich profiling of the identified segments, revealing gaps and opportunities for marketers.

This research presents a new approach to segmentation, which surmounts the restrictions of segmentation methods dependent on survey-based information. It contributes to the field and provides a valuable means for conducting customer-focused market segmentation. Furthermore, the suggested methodology is transferable across different sectors and not reliant on particular data sources, creating possibilities in diverse scenarios.