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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.

This paper aims to shed light on the linkage between research and development (R&D) networks and public funding presented in a given period by using network-based evaluation tools as a means of exploring the relational dimension in public projects designed to foster technology R&D activities.

This research uses co-occurrence network analysis of relevant public projects to assess how technological associations might occur within the R&D activities of given publicly funded projects as well as conducts correlation analysis to understand the extent to which linkages of R&D activity in technology fields are related to public expenditure.

Core technology fields, regarded as eligible to receive continued public funding, are critical for enhancing competitiveness and sustainable growth at the nationally strategic technology level. Thus, the relationship between R&D and the level of government funding for these fields is generally perceived as strong. However, a few technology fields, which did not actively form specific network relationships with other technology fields, are considered to exceptionally drive the largest government support. This trend indicates that the government-funded R&D should be designed and managed not only to curb the inefficiencies existing in the current funding programs but also to achieve the appropriateness for further technology development.

Despite the comprehensive findings, this study has several limitations. First, it is difficult to control any confounding factors, such as the determinants and constraints of the government budget allocation and expenditure decisions over S&T areas, strategic frameworks for public investment and evolving policy landscapes in technology sectors, which lead to bias in the study results. Second, this study is based on a narrow, single-year data set of a specific field of projects supported by the Korean government’s R&D program. Therefore, the generalization of findings may be limited. The authors assumed that influences caused by confounding variables during the initial phase of the public funding schemes would not be significant, but they did not take into account possible factors that might arise coincident with the subsequent phase changes. As such, the issue of confounding variables needs to be carefully considered in research design to provide alternative explanations for the results that have been ruled out. The limitations of this study, therefore, could be overcome by comparing the outcome difference between subsidized and non-subsidized R&D projects or evaluating targeted funding schemes or tax incentives that support and promote various areas of R&D with sufficiently large, evidence-based data sets. Also, future research must identify and analyze the R&D activities concerning public support programs performed in other countries associated with strategic priorities to provide more profound insight into how they differ. Third, there are some drawbacks to using these principal investigators-provided classification codes, such as subjectivity, inaccuracy and non-representation. These limitations may be addressed by using content-based representations of the projects rather than using pre-defined codes. Finally, the role that government investment in R&D has played in developing new science and manufacturing technologies of materials and components through network relationships could be better examined using longitudinal analysis. Furthermore, the findings suggest the need for further research to integrate econometric models of performance outcomes such as input–output relations into the network analysis for analyzing the flow of resources and activities between R&D sectors in a national economy. Therefore, future research would be helpful in developing a methodological strategy that could analyze temporal trends in the identification of the effects of public funding on the performance of R&D activity and demand.

Public funding schemes and their intended R&D relationships still depend on a framework to generate the right circumstances for leading and promoting coordinated R&D activities while strengthening research capacity to enhance the competitiveness of technologies. Each technology field has a relatively important role in R&D development that should be effectively managed and supervised to accomplish its intended goals of R&D budgeting. Thus, when designing and managing R&D funding schemes and strategy-driven R&D relations, potential benefits and costs of using resources from each technology field should be defined and measured. In this regard, government-funded R&D activities should be designed to develop or accommodate a coordinated program evaluation, to be able to examine the extent to which public funding is achieving its objectives of fostering R&D networks, balancing the purpose of government funding against the needs of researchers and technology sectors. In this sense, the examination of public R&D relations provides a platform for discussion of relational network structures characterizing R&D activities, the strategic direction and priorities for budget allocation of the R&D projects. It also indicates the methodological basis for addressing the impact of public funding for R&D activities on the overall performance of technology fields.

The value of this work lies in a preliminary exploratory analysis that provides a high-level snapshot of the areas of metallurgy, polymers/chemistry/fibers and ceramics, funded by the Korean Government in 2016 to promote technological competitiveness by encouraging industries to maintain and expand their competencies.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.

This study examines the research that has been conducted on user-generated advertising content in the social marketing strategies of commercial brands to understand the phenomenon, explore academic interest in the topic and identify areas of limited thematic coverage.

A systematic review of existing scientific literature in the Web of Science (WoS) and Scopus was carried out using the PRISMA protocol. A co-occurrence matrix was used to review emerging topics on user-generated content (UGC) and influencer marketing, allowing the identification of articles (n = 59) related to the objective of this research.

Most research has analyzed UGC in images or text, but only very few have addressed videos and other digital formats (such as reels, image carousels or podcasts), although there is sufficient work focused on Twitter, Facebook and YouTube. There was no evidence of work exploring the effects, repercussions and possible dangers of uncontrolled brand exposure through Unofficial Brand Ambassadors.

The literature review has allowed finding important areas of future research that the scientific community has not sufficiently addressed. Likewise, this work shows structurally several classifications of UGC, which will facilitate future research to deepen and broaden these categories.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places.

Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons.

Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers.

Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere.

This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

本研究旨在使用自下而上的归纳方法从大量的在线消费者的叙述中总结出目的地形象的属性, 并根据目的地形象的属性和地点之间的关系建立一个目的地分类系统。

首先通过内容分析方法和社会网络分析方法分析在线消费者的叙述数据得出目的地的消费者形象结构, 然后采用聚类分析方法按照属性对目的地形象进行分组, 并通过方差分析进行比较。

结果显示总结出22种属性, 并将其组合为三组(核心、预期和潜在)。目的地根据其网络中心度被分为三个集群(综合城市、风景和生活方式)。最常被提及的(核心)属性是景观、目的地的交通、食品和饮料以及资源型景点。在消费者的叙述数据中表明社会生活是有意义的, 但常常被研究人员忽视。

首先本研究通过将自下而上的归纳研究设计与大数据相结合对中国旅游业进行了实证研究。其次通过将49个旅游目的地分为三类以及基于大数据建立了一个新的旅游目的地分类系统。

旅游目的地应该明确自己属于哪一类目的地类型然后迎合游客的真正需求。DMOs应该提供旅游目的地的基本属性, 注重提升旅游目的地的社会生活氛围。

Se realizó un análisis de contenido en redes sociales para explorar la estructura de la imagen de los destinos por parte de los consumidores basándose en las descripciones en línea. A continuación, se empleó el análisis de clusters para agrupar los destinos por atributos, estableciendo comparaciones mediante el análisis ANOVA.

Los propósitos de esta investigación eran utilizar un enfoque ascendente e inductivo para obtener atributos de imagen de los destinos a partir de grandes cantidades de descripciones de consumidores en línea, y establecer un sistema de clasificación de destinos basado en las relaciones entre atributos y lugares.

Se identificaron 22 atributos que luego se agruparon en tres grupos (principales, esperados, latentes). Los destinos se clasificaron en tres grupos (urbano integral, paisajístico y de estilo de vida) en función de sus centralidades de red. Utilizando datos sobre el turismo chino, se determinó que los atributos (centrales) más mencionados eran el paisaje, el tráfico dentro del destino, la comida y las bebidas, y las atracciones basadas en los recursos. La vida social era importante en los comentarios de los consumidores, pero a menudo los investigadores la pasaban por alto.

Los destinos deberían determinar a qué categoría pertenecen y luego apelar a las necesidades reales de los turistas. Los DMO deberían proporcionar los atributos esenciales prestando mayor atención a resaltar el entorno de vida social de los destinos.

Esta investigación elaboró un trabajo empírico sobre el turismo chino combinando un diseño de investigación inductiva ascendente con big data. Dividió los 49 destinos en tres categorías y estableció un nuevo sistema basado en los grandes datos para clasificar los destinos turísticos.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

The article attempts to shed light on the social aspects of research that deal with Sustainable Development Goals (SDGs) and sustainable cities. The aim is to offer a global view of these facets' evolution and to provide information on people-centered smart cities.

The research is qualitative. A systematic bibliometric approach is a framework for the research. The unit of analysis is publications on SDGs and Smart Cities (SCs) indexed in Scopus. The authors used VOSviewer text mining functionality to construct co-occurrence networks of socially related critical terms extracted from textual data. The co-occurrence of keywords presents a valuable method and process for attaining in-depth analysis and fast comprehension of trends and linkages in articles from a holistic approach.

Social media, social sustainability and social capital are the three multifaceted social keywords that co-occur in SDGs and SCs. The paper provides a brief compendium of resources and frameworks to build a socially sustainable smart city.

The retrieval date was on 15 August 2019. The authors used the same search query for new papers released in 2019 and afterwards to update their findings. The authors collected 657 documents on SCs, compared to 2,975 documents about SDGs demonstrating that their findings are still trending in the same direction, emphasizing the importance of the research topic. SCs' social aspects are still chartered areas that require the attention to future research.

The authors’ decision to use two separate data sets for SCs and SDGs data files helps to provide a more comprehensive picture of the research landscape. It may identify areas where research is lacking or needs future research. The authors present an integrative agenda for a smart city to be socially sustainable. Innovative approaches to urban planning are required to empower the place and context and improve the users' satisfaction, where innovative solutions enable smart, sustainable and inclusive societies. Infrastructure governance is a critical keystone. It could guarantee that public investments contribute to sustainable urban development while enhancing city resilience, particularly in facing climate change and inclusive growth challenges.