Research on the semantic-based co-word analysis

A bibliometric analysis of telework research based on Web of Science via VOSviewer

BACKGROUND

The COVID-19 pandemic has sparked increased interest in telework as a means of reducing the spread of the virus and maintaining social distance.

OBJECTIVE

This study aims to generate a bibliometric analysis of research progress and trends in telework over the past 20 years.

METHOD

A search of key terms was conducted in the Social Science Citation Index, Science Citation Index Expanded, and Arts and Humanities Citation Index categories for documents published on telework from 2000-2023. A total of 3,446 studies were analyzed using VOSviewer for co-citation, co-word, and cluster analysis.

RESULTS

Bibliometric analysis revealed that telework research has experienced a significant increase during the COVID-19 pandemic, with the number of publications in 2022 being more than 15 times higher than that in 2019. The analysis revealed that the most commonly researched areas related to telework were applied psychology, management and business. The knowledge base focuses on the antecedents, moderators, mediators, and consequences of telework, and the research primarily centers around seven directions of well-being, mental health, and work-family conflict. A conceptual framework for telework research and suggestions for future investigation are proposed based on the results of the bibliometric analysis.

CONCLUSION

This study provides an overview of telework research over the past two decades, highlighting the current status and hot topics in the field. It calls for wider and more active participation of researchers globally to advance the understanding of telework.

Survey on sentiment analysis: evolution of research methods and topics

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

Towards Holistic Governance of China's E-Waste Recycling: Evolution of Networked Policies

Electronic products are being updated and replaced much faster and there is therefore an increasing growth in electronic waste (e-waste). In order to promote professional recycling of e-waste, the relevant government departments of China have published a series of policies. This paper aims to unearth the evolution tendency of the networked policies towards holistic governance of China’s e-waste recycling. Content analysis, quantitative text analysis and network analysis are applied to analyze relevant policy documents from 2001 to 2016. This paper illustrates evolution of policy themes, evolution of intergovernmental relationships, and evolution of policy relations. This study reveals policy intentions, maps policy progress, and unearths governance philosophy, providing an overall understanding of the policy ways by which the Chinese government has deployed its guiding strategies on professional recycling of e-waste. This paper illustrates how to approach holistic governance from perspective of networked policies, contributing to answering the central question of holistic governance about how to achieve it.

Exploring the topic structure and evolution of associations in information behavior research through co-word analysis

This study aims to reveal the distribution of topics, and the associations among them, in information behavior research from 2009 to 2018. Working with a collection of 6744 publications from the Web of Science database, co-word analysis is used to investigate the overall topic structure, the associations among the topics, and their evolution in different years, which is supplemented by visualization with science maps. The results uncovered an unbalanced distribution of topics, and that the topics cluster into six communities representing subdivisions of this field: information behavior in patient-centered studies; information interaction in the digital environment; information literacy in health and academic contexts; health literacy on the Internet; information behavior in child-centered studies; and information behavior in medical informatics. The findings supplement and provide refinements to work on the state of this field, and help researchers obtain an overview of the past decade to guide their future work.

Evolution of Marine Environmental Governance Policy in China

This paper aims to unearth the ways in which the Chinese government applies policies to govern the marine environment effectively. Co-word analysis, word frequency analysis and multi-dimensional scaling (MDS) were chosen to analyze the evolution of marine environmental policy. This paper focuses on the marine environmental governance policy of China since 1982, takes the five-year plan for marine economic development as the node, divides these policies into five stages: The germination period, the 10th Five-Year Plan period, the 11th Five-Year Plan period, the 12th Five-Year Plan period and the 13th Five-Year Plan period. The evolution characteristics of China’s marine environmental governance policy are analyzed accordingly and include the diversification of participants, changes from ex-post control to ex-ante control, diversification of policy tools, and expansion of governance scope. Finally, we elucidate the challenges regarding the formulation and implementation of China’s marine environmental governance policies in the future.