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Enabling the public to grasp the state and trends of emerging technologies facilitates technology adoption, socioeconomic investment, and business growth. Science journalism is crucial in connecting the scientific community with the public. This study focuses on the metaverse due to its rapid expansion. To validate knowledge extraction, we analyze relevant metaverse news from TechCrunch.com, covering 2020 to 2023, to build the domain knowledge schema. This study introduces a novel approach that combines Latent Dirichlet Allocation (LDA) topic modeling with bibliometrics as a computational intelligent method to discover topics and construct knowledge based on technology news framing. LDA is used to identify the topics in metaverse news, while a bibliometrics method, i.e., co-word networks analysis, clarifies term association strength and visualizes the findings. The study outlines the seven key elements highlighted in technology journalism, which shape public perception and expectations of new technologies. These elements offer insights into the factors influencing the development, diffusion, and adoption of new technologies. The extracted representative terms and enterprises from eight topics help map the knowledge architecture diagram for the domain. The research contributes to helping stakeholders systematically understand metaverse technology topics and demonstrate collaborative partnerships between enterprises.

Using 12 104 publications from 2014 to 2023 in the DeepBone database, this study employs bibliometric methods, including full-text latent Dirichlet allocation (LDA) modelling, co-occurrence network analysis and geographic mapping with ArcGIS, to examine three key aspects of vertebrate palaeontology development: geographic distribution of newly established taxa, gender demographics among researchers and research trends. Gender data were analysed using automated tools with manual verification to ensure accuracy, while methodological evolution was investigated through systematic text mining and classification. Among 8336 newly established taxa, mammals (34.72%) and fishes (29.76%) dominate, followed by reptiles (25.34%), birds (7.39%) and amphibians (2.80%). Geographic analysis reveals significant regional disparities, with the USA (13.50%) and China (13.32%) contributing the most, while Africa and Oceania remain under-represented (less than 10%). Gender analysis indicates a gradual increase in female representation from 22.78 to 27.20% over the decade, highlighting the imperative to address gender disparities in vertebrate palaeontology, thereby advancing equity in alignment with UNESCO Sustainable Development Goal 5. LDA topic modelling identifies 15 distinct research topics, encompassing evolutionary biology, cranial and skeletal morphology, dinosaur–bird evolution and human evolution, while co-occurrence analysis highlights the evolution of research methodologies, revealing strong interconnections between phylogenetic analysis (15%), traditional morphological analysis (12%) and high-resolution imaging techniques (9%).

With the growth of the wellness tourism industry, cultural tourism destinations offering authentic experiences for well-being are attracting increasing attention. In this context, this study aims to explore the place experiences of Templestay, Korea's representative cultural and authenticity-seeking tourism destination, through an analysis of participant reviews. It seeks to contribute to strengthening Templestay's spatial characteristics as a destination pursuing authenticity. Unstructured data were collected from portal sites and SNS travel reviews of Templestay, with 22,736 pieces of data analyzed through text refinement. Text mining and latent Dirichlet allocation (LDA) topic modeling analysis were employed on the collected textual big data. The LDA topic modeling analysis revealed two topics related to Templestay's place experiences: spatial activity and physical environment, highlighting tourists' perception of authenticity. Leveraging these findings in destination marketing strategies for Templestay can offer customized information for authenticity pursuits and enhance its appeal as cultural tourism. This study contributes to the theoretical understanding of tourism studies and the development of the wellness tourism industry as a sustainable tourism model by empirically analyzing the place experiences of Templestay as a tourism destination oriented toward authenticity using big data.

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry.

With the growth of the wellness tourism industry, cultural tourism destinations offering authentic experiences for well-being are attracting increasing attention. In this context, this study aims to explore the place experiences of Templestay, Korea’s representative cultural and authenticity-seekingtourism destination, through an analysis of participant reviews. It seeks to contribute to strengthening Templestay’s spatial characteristics as a destination pursuing authenticity. Unstructured data were collected from portal sites and SNS travel reviews of Templestay, with 22,736 pieces of data analyzed through text refinement. Text mining and latent Dirichlet allocation (LDA) topic modeling analysiswere employed on the collected textual big data. The LDA topic modeling analysis revealed two topics related to Templestay’s place experiences: spatial activity and physical environment, highlighting tourists’ perception of authenticity. Leveraging these findings in destination marketing strategies for Templestay can offer customized information for authenticity pursuits and enhance its appeal as cultural tourism. This study contributes to the theoretical understanding of tourism studies and the development of the wellness tourism industry as a sustainable tourism model by empirically analyzing the place experiences of Templestay as a tourism destination oriented toward authenticity using big data.

This study used the Latent Dirichlet Allocation (LDA) topic model to analyze pre-service teachers’ views on the nature of science (NOS). This approach can be used to automate the classification of documents, and at the same time, the researcher does not need to deduce with a NOS framework prior to evaluation. Participants were 155 pre-service teachers studying at the Shandong Normal University in China. To gather our data, we used an open questionnaire, namely, the Views of Nature of Science Questionnaire—Form C (VNOS-C). LDA topic modeling was used to classify the document, which was divided into 12 topics. By comparing the LDA topic modeling results with the theoretical framework behind the VNOS-C questionnaire, we categorized these 12 topics into eight descriptive aspects of the NOS: The Empirical Nature of Scientific Knowledge, Observation, Inference, and Theoretical Entities in Science, Scientific Theories and Laws, The Theory-Laden Nature of Scientific Knowledge, The Social and Cultural Embeddedness of Scientific Knowledge, The Myth of The Scientific Method, The Tentative Nature of Scientific Knowledge, and The Nature of Scientific Theory. The results show that pre-service teachers usually hold naive or mixed views of the NOS. In addition, each aspect of NOS is not independent of each other but interrelated and influencing each other. In the future, more consideration can be given to the relationship between each aspect of NOS.

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.