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Vie, Stephanie. "Keywords: Qualitative Research." Community Literacy Journal 5, no. 1 (2010): 175–80. http://dx.doi.org/10.1353/clj.2010.a471052.
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Yodprechavigit, Krisanapong, and Sukree Sinthupinyo. "Detecting Upcoming Patent Keywords by Predicting Keyword Trends Using Patent Keyword Network." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 2 (2024): 195–206. http://dx.doi.org/10.37936/ecti-cit.2024182.255029.
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In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.
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Lepecki, André. "Dance Discourses: Keywords In Dance Research." Dance Research Journal 44, no. 1 (2012): 95–99. http://dx.doi.org/10.1017/s0149767712000010.
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The formation of what Randy Martin has called “critical dance studies” (1998) has gained increased momentum over the past decade. Martin's notion of critical dance studies clarified how dance scholarship was being reshaped by the explicit inclusion of critical theory in its methodologies and epistemologies. One of the major consequences for dance studies in embracing critical theory was the identification of dancing and choreographic practices as being also theory. Understanding dance as theory is not equivalent to seeing dance as the sole provider of the theoretical tools it needs for its own analysis (this would be intolerably solipsistic). Rather, it means that dance becomes a privileged practice ready to provide analytical tools for theorizing other areas of social performances: politics, culture, formations of disciplines and their bodies (docile or resistant). Dance is a mode of theorization that theory itself would need in order to address the social and political problematic brought by issues close to dance such as mobilization, embodiment, subjectivities, participation, representation, desire, discipline, control, etc.
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Lee, Danielle. "Analyzing Research Productivity and Keywords of Korean ‘Art’ Research." Korean Society of Science & Art 39, no. 2 (2021): 303–14. http://dx.doi.org/10.17548/ksaf.2021.03.30.303.
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Pearce, Lynne. "Research terms explained from keywords to RCTs." Nursing Older People 36, no. 5 (2024): 16–17. http://dx.doi.org/10.7748/nop.36.5.16.s7.
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Shimbaru, Toshiki, and Naoto Kai. "Comparison of Research Performance by Institute based on Research Keywords." International Journal of Institutional Research and Management 7, no. 1 (2023): 1. http://dx.doi.org/10.52731/ijirm.v7.i1.778.
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Pojanapunya, Punjaporn. "Clustering Keywords to Identify Concepts in Texts: An Analysis of Research Articles in Applied Linguistics." rEFLections 22 (February 17, 2018): 55–70. http://dx.doi.org/10.61508/refl.v22i0.112328.
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Keyword analysis is one of the most widely used methods in corpus linguistics. The method is used to generate keywords which provide an indication of concepts in texts or a corpus. Keyword analysis tools commonly produce resulting keywords presented as a list which rather poorly indicates what the corpus is about since it typically requires analysts’ knowledge on conceptual associations between keywords. Therefore, common follow-up methods of keyword analysis are to examine concordances, collocational patterns, and some other patterns of associations between keywords and contexts. This study focuses on the association within a group of keywords by constructing a representation of a keyword list as keyword clusters. The keywords for an analysis were generated from two corpora; the target corpus was collected from research articles in applied linguistics and the comparative corpus was a collection of research in pure and applied sciences. The relationship between the top 30 keywords was identifed using mutual information scores of all possible pairs of the keywords within a span of 20 and these scores were used as input for creating keyword clusters. The representations of the 30 keywords as a list and clusters are presented and discussed.
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Kim, Ae-Kyung. "Keyword Network Analysis of Research Trends in Makeup." Asian Journal of Beauty and Cosmetology 22, no. 3 (2024): 415–25. http://dx.doi.org/10.20402/ajbc.2024.0034.
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Purpose: This study aims to identify research on makeup-related topics from the past decade using keyword network analysis.Methods: This study used keyword network analysis to extract data from the Korea Citation Index journal database of the National Research Foundation of Korea. A total of 585 papers related to makeup, published between 2013 and 2022, were used for the analysis.Results: An examination of the frequency of makeup-related keywords indicated that “image” displayed the highest frequency, followed by “art makeup,” “design,” “cosmetics,” “color,” and “semi-permanent makeup.” The study then classified the year of publication into the first (2013–2017) and second (2018–2022) periods. The terms “image,” “color,” “design,” and “art makeup” and “cosmetics,” “image,” “art makeup,” “design,” and “semi-permanent makeup” were studied in these orders during the first and second periods, respectively. Third, the study explored the properties of keywords using centrality analysis. The results indicated that the keyword with the most interaction with other keywords is “image,” whereas “design” and “art makeup” are keywords with a high centrality of connection with other keywords used in makeup-related research. Thus, the result implies that this topic is important.Conclusion: Research related to makeup is being conducted on various topics, including image, cosmetics, color, art, behavior, service, and education. However, convergence research, such as the Fourth Industrial Revolution and artificial intelligence (AI), is being conducted, and keywords related to this are expected to appear in several cases in the future.
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Ni, Pin, Yuming Li, and Victor Chang. "Research on Text Classification Based on Automatically Extracted Keywords." International Journal of Enterprise Information Systems 16, no. 4 (2020): 1–16. http://dx.doi.org/10.4018/ijeis.2020100101.
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Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.
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Komleh, Ramin Abolghasemi. "Investigation of Relationship Between Google Cost-Per-Click and Search-Volume on Keyword of Chicago Tours." Journal of Information Systems and Informatics 5, no. 3 (2023): 1002–19. http://dx.doi.org/10.51519/journalisi.v5i3.518.
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Pay-per-click is one of common and important ways in online advertising through Google; but some keywords are usually expensive and this issue is visible through Google cost-per-click in Google keyword planner tool. It has done many research work in Google advertising formats, but place of studies in field of relationship between Google cost-per-click and keyword search-volume, especially in tourism area is blank. This paper tries to answer of this main question that is there a significant relationship between cost-per-click and keyword search-volume and the number of keyword's word? In other words, does cost-per-click increase or decrease, based on keyword search-volume and the number of keyword's word (multi-word keyword or hyper focused keyword phrases)? Chicago auspicates a mighty increase in tourism 2022 as its announcement an 86 percent growth in visitors in 2021 compared to 2020 and choosing the right keyword in Google search engine by travel agencies can lead to more sales. In this research, I sampled 100 search result through combination of ″chicago+tours″ as a search keyword on google.com (Google USA) and Google keyword planner daily data (December 26, 2022) based on the last 7-10 days, and via regression analysis with least squares and loess model, I tried to investigate the relationship between search-volume and cost-per-click and it will help scholars in future research in this area. This study was carried out within the scope of selected keyword in tourism area in form of cross-sectional. I find that there is no significant relationship between short-tail keywords and cost-per-click. The result of this research shows that some medium or long-tail keywords are more expensive than short-tail keywords with more search results. Another result that was observed is the lack of significant relationship between keyword search-volume and its cost-per-click, so that in some cases, high-search keywords are cheaper than keywords with a low-search.
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Yun, An-Soon, and Hyo-Young Park. "Emotional Intelligence Research Trend Analysis: Using Keyword Network Analysis." Korean Journal of the Human Development 31, no. 1 (2024): 67–84. http://dx.doi.org/10.15284/kjhd.2024.31.1.67.
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The purpose of this study is to analyze trends in emotional intelligence research conducted in Korea. For this purpose, we collected papers using major online sites and in-tegrated papers by excluding duplicate papers. The integrated data went through a pre-processing process based on data cleaning, and keywords were extracted. The frequency of appearance of papers for each keyword was checked, and the degree centrality, betwe-enness centrality, closeness centrality, and eigenvector centrality of the keyword network were examined. As a result of the study, ‘control’, ‘infants’, ‘education’, and ‘ability’ were keywords that frequently appeared in emotional intelligence studies. In addition, ‘control’, ‘education’, and ‘ability’ were found to be the main keywords from the early to the late pe-riod, but there was some difference in the centrality ranking. ‘Positive’ appeared as a key-word with a notable change in centrality ranking. In this study, we identify keywords that frequently appear in emotional intelligence research through keyword network analysis and identify changes in core keywords depending on the research period. This study will serve as helpful data for conducting follow-up research on emotional intelligence.
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Kim, Min-Chang. "Exploring the Keyword Network Analysis and Research Trends for Sustainable Development of Paralympic Research: Based on Changes in Classification." Applied Sciences 13, no. 5 (2023): 3050. http://dx.doi.org/10.3390/app13053050.
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This study aims to identify an overall keyword network by drawing on core keywords in Paralympic studies and analyzing the relationships. It also aims to provide information on the changes in Paralympic study trends through a time series analysis to understand change, according to the application of a new classification (evidence-based classification). This study made a bibliographic list of 1613 studies by collecting studies published between 2003 and 2022 using keyword network analysis (KNA). By refining to exclude unsuitable studies, 1404 studies and 6414 keywords were used for the analysis. As a result of analyzing entire Paralympic studies performed between 2003 and 2022, Paralympic studies were revealed to be on the rise. The entire Paralympic studies ascertained that the areas of study were mainstream and centered on the top three keywords: athlete; performance; and classification. Secondly, as a result of analyzing Paralympic study trends in a time series manner, based on 2017 when a new classification was applied, studies related to physical impairment centered on the top two keywords, athlete and performance, were mainstream in Section 1 (2003–2016). Olympic Games, performance, and wheelchairs formed detailed study areas. In the Paralympic studies in Section 2 (2017–2022), the relevant studies centered on the top two keywords, athletes and performance, were mainstream. Parasport, disability, and classification were ascertained to form detailed study areas. Through all this, the changed trend in the keyword Paralympics was confirmed. The results of this study are meaningful in that they can contribute to the expansion of the Paralympic areas of study and invigoration by exploring core keywords and providing relevant information based on the significant change point in time, including entire Paralympic study trends.
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Güven, Esra, Mustafa Hamalosmanoğlu, Zehra Kaplan, and Serdar Varinlioğlu. "Analysis of keywords used in environmental education research." Pegem Eğitim ve Öğretim Dergisi 4, no. 3 (2014): 73–82. http://dx.doi.org/10.14527/pegegog.2014.017.
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The purpose of this study is to examine keywords in articles and dissertations published in the field of environmental education. The study was carried out on 48 articles and 73 dissertations published between 2007-2011 in the field of environmental education. The data of this study were collected from document analysis. These collected data were analyzed from content analysis. In this study, it was seen that 192 distinct keywords were used in examined articles and dissertations, "environmental education" (f=76), "environment" (f=20), "attitude toward the environment" (f=19) and "environmental awareness" (f=13) were the most used keywords. In addition to, it was determined that the frequencies of nearly all keywords were low.
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Li, Ya Min, and Xian Huan Zhang. "The Research and Implementation of Keyword Extraction Technology." Applied Mechanics and Materials 644-650 (September 2014): 2003–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2003.
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Keyword extraction plays an important role in abstract, information retrieval, data mining, text clustering etc. Extracting the keywords from a document can increases the efficiency of retrieval, thus provide great help to efficiently organize the resource. Few writers on the Internet have given the keywords of a document. Artificially extracting the keywords of a document is a great deal of work, so we need a method of extracting the keywords automatically. The paper constructing a verb, function words, stop words etc. small library from the perspective of the Chinese part of speech, realize rapid word segmentation based on the research, analysis, improvement of traditional lexical maximum matching points, and analyze, realize extracting the keywords based on TFIDF(Term Frequency Inverse Document Frequency).
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Isenberg, Petra, Tobias Isenberg, Michael Sedlmair, Jian Chen, and Torsten Moller. "Visualization as Seen through its Research Paper Keywords." IEEE Transactions on Visualization and Computer Graphics 23, no. 1 (2017): 771–80. http://dx.doi.org/10.1109/tvcg.2016.2598827.
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Park, Dongpill, and JiHye Yang. "Analysis of ‘Tamna Sullyeokdo’ Research Trends using Keyword Network Analysis." Association of Korean Cultural and Historical Geographers 36, no. 3 (2024): 59–73. https://doi.org/10.29349/jchg.2024.36.3.59.
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This study was conducted to identify the research trends of previous studies on the topic of Tamna Sullyeokdo using 75 studies. Keyword network analysis, a methodology that identifies the importance of keywords and relationships between keywords based on the frequency of occurrence and co-occurrence of keywords extracted from each study was used as a methodology for analysis. The research results showed that the most frequently occurrence keyword was ‘Tamna Sullyeokdo’ was the highest, and the degree centrality, closeness centrality, and betweenness centrality were also highest. The significance of this research lies in providing an opportunity to understand the overall achievements of prior studies on ‘Tamna Sullyeokdo’ in various academic fields and content categories. Additionally, the utilization of keyword network analysis using research keywords allowed for an understanding of the content and structure that has been prominently addressed in studies related to ‘Tamna Sullyeokdo’. The individual importance of research keywords and the relationships between them were also identified. One limitation of this research is the inability to analyze the entire body of research related to ‘Tamna Sullyeokdo’, resulting in the omission of trends in excluded studies and the inability to provide a comprehensive overview of all studies.
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Zhang, Juan, Qi Yu, Fashan Zheng, Chao Long, Zuxun Lu, and Zhiguang Duan. "Comparing keywords plus of WOS and author keywords: A case study of patient adherence research." Journal of the Association for Information Science and Technology 67, no. 4 (2015): 967–72. http://dx.doi.org/10.1002/asi.23437.
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Cantos‐Mateos, G., B. Vargas‐Quesada, Z. Chinchilla‐Rodríguez, and M. A. Zulueta. "Stem cell research: bibliometric analysis of main research areas through KeyWords Plus." Aslib Proceedings 64, no. 6 (2012): 561–90. http://dx.doi.org/10.1108/00012531211281698.
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Ozek, Burcu, Zhenyuan Lu, Fatemeh Pouromran, Srinivasan Radhakrishnan, and Sagar Kamarthi. "Analysis of pain research literature through keyword Co-occurrence networks." PLOS Digital Health 2, no. 9 (2023): e0000331. http://dx.doi.org/10.1371/journal.pdig.0000331.
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Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.
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Oh, Yun-Kyoung. "Analysis of Research Trends in Skin Care using Keyword Network Analysis." Journal of the Korean Society of Cosmetology 28, no. 6 (2022): 1308–17. http://dx.doi.org/10.52660/jksc.2022.28.6.1308.
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This study tried to examine the research trends related to skin care by using keyword network analysis on data extracted from the KCI journal D/B provided by the National Research Foundation of Korea. Text refinement was performed by extracting key words from a total of 504 papers published from 2011 to August 2022. The tools used for analysis are Textom and UCINET6. As a result, first, the keyword frequency was ‘cosmetics’ the most, followed by‘appearance management behavior’, ‘skin care shop’, ‘satisfaction’, ‘female’, ‘medical’, ‘service’, and ‘acne’. Second, as a result of examining the frequency of skin care related keywords according to the study period, the 1st period (2011-2016) and the 2nd period (2017-2022) both included ‘cosmetics’ as the keyword the most. In the second period, keywords such as ‘skin care shop’, ‘medical’, ‘aging’, and ‘acne’ appeared in high rankings. Third, keywords such as ‘cosmetics’, ‘satisfaction’, ‘skin care room’, ‘appearance management behavior’ and ‘service’ are keywords with high connection centrality, proximity centrality, and dominance centrality. It was confirmed that it was actively progressing.
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Nam, Gue, and Youjung Kim. "Analysis of research trends on children's picture books using keyword network analysis." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 7 (2023): 641–62. http://dx.doi.org/10.22251/jlcci.2023.23.7.641.
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Objectives The purpose of this study is to analyze the research trends of early childhood and picture books using keyword network analysis.
 Methods To this end, based on the academic database, keywords from 1,195 academic papers related to early childhood and picture books published from 2001 to 2020 were collected, and data analysis was conducted by keyword network analysis (keyword appearance frequency, connection centrality, and mediation centrality).
 Results First, from 2001 to 2020, creativity and pro-social behavior keywords among early childhood and picture book-related keywords were all ranked high in appearance frequency, connection centrality, and mediation centrality. On the other hand, research trends, literature, picture book making, and reading interest keywords were analyzed higher in connection-centeredness and mediation-centeredness than in the frequency. Second, for network analysis by period of early childhood and picture book research, it was divided into 1st (2001-2010) and 2nd (2011-2020), and the frequency of keyword appearance was high in the order of creativity, language ability, language expression, and picture book reading. In addition, the first period showed a high frequency of language appearance such as story composition ability and story comprehension, and the second period showed a high frequency related to society and emotions such as emotional intelligence, view acceptance, and self-esteem. Keyword connection centrality was high in the first period, keywords related to language ability, picture book making, and picture book reading, and research trends and social emotion-related keywords were high in the second period.
 Conclusions Research trends related to early childhood and picture books show a tendency that studies related to the literary nature of picture books have been studied from various angles. The keywords that appeared in connection-centeredness and mediation-centeredness in this study were different by period in network analysis by period. It can be seen that this is an important subject for each period.
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Tripathi, Manorama, Sunil Kumar, S. K. Sonker, and Parveen Babbar. "Occurrence of author keywords and keywords plus in social sciences and humanities research : A preliminary study." COLLNET Journal of Scientometrics and Information Management 12, no. 2 (2018): 215–32. http://dx.doi.org/10.1080/09737766.2018.1436951.
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Swacha, Jakub. "Topic Evolution in the Research on Educational Gamification." Education Sciences 12, no. 10 (2022): 640. http://dx.doi.org/10.3390/educsci12100640.
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The research on educational gamification spans many topics of interest. As the total volume of research in this area has greatly increased in the last 10 years, it is interesting to see how the interest in the respective topics has changed over the same period. In this paper, we answer this question by means of keyword analysis performed on 7572 unique keywords extracted from 2203 papers. The obtained results reveal (1) the high popularity of keywords that are non-obviously relevant to gamification, (2) vast disproportions in the volume of research dedicated to different aspects of the same research sub-area, and (3) differing patterns of popularity among the most frequent keywords, as well as keywords introduced and abandoned in recent years (4). The presented findings bear a number of implications for the future of research on educational gamification.
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Cho, Soo Jin, and Tae-Gang Kim. "A Study on Research Trends in 『Audiology and Speech Research』 Using Keyword Network Analysis." Audiology and Speech Research 19, no. 1 (2023): 28–42. http://dx.doi.org/10.21848/asr.220085.
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Purpose: The purpose of this study was to investigate the research trends of papers published in ‘Audiology and Speech Research’ and to obtain implications for the direction of research.Methods: Data were collected from 2005 to 2022 articles research information sharing service (RISS) which is provided by Korea education and Korea Citation Index (KCI). After extracting and cleansing keywords, the frequency, centrality, and visualization data analysis for keywords were performed using KrKwic & NodeXL programs.Results: According to the results of keyword frequency analysis, ‘hearing aid’ showed the highest frequency, followed by ‘hearing loss’, ‘noise’, and ‘cochlear implant’, etc. As a result of the keyword centrality analysis, it was found that the centrality was particularly high in ‘hearing aid’, ‘hearing loss’, ‘word recognition’, ‘cochlear implant’, ‘sensorineural hearing loss’, ‘auditory training’, ‘aural rehabilitaion’.Conclusion: The results of this study will provide useful data for understanding current research trends in ASR and further suggesting new research directions in the field of audiology and speech pathology.
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Zhang, Liping, Jing Li, and Song Li. "Research on Time-Aware Group Query Method with Exclusion Keywords." ISPRS International Journal of Geo-Information 12, no. 10 (2023): 438. http://dx.doi.org/10.3390/ijgi12100438.
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Aiming at the problem that the existing spatial keyword group query problem did not consider the query requirements with exclusion keywords and time attributes, a time-aware group query problem with exclusion keywords (TEGSKQ) is proposed for the first time. To solve this problem effectively, this paper proposes a query method based on the EKTIR-Tree index and dominating group (EKTDG). This method first proposes the EKTIR-tree index, which incorporates Huffman coding and integrates Bloom filters to deal with excluded keywords in order to improve the hit rate of keyword queries, significantly improving the query efficiency and reducing the storage occupancy. Then, the Candidate algorithm is proposed based on the EKTIR-tree index to filter out the spatial–textual objects that meet the query’s keywords and time requirements, narrowing the search space for subsequent queries on a large scale. To address the problem of the low efficiency of existing algorithms based on a spatial distance query, a distance-dominating group is defined and a pruning algorithm based on a spatial distance-dominating group is proposed, which is a refining process of query results and greatly improves the search efficiency of the query. Theoretical and experimental studies show that the proposed method can better handle group queries with exclusion keywords based on time awareness.
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Sugiyanto, Eko, Edi Purwanto, Indah Rahmadani, et al. "Understanding Research Dynamics in Job Stress." International Journal of Religion 5, no. 6 (2024): 12–21. http://dx.doi.org/10.61707/2gpmrh71.
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The objective of this analysis is to conduct a bibliometric analysis to map the research on work stress. This will be done by employing quantitative methods such as network, overlay, and density cluster models to analyze the research on work stress over a span of 11 years. Data are extracted from the scientific literature in the Science Direct database using the keyword "job stress," in which a total of 180 relevant articles were identified. These articles were analyzed using algorithms in Vosviewer to identify common topics in stress research. The analysis revealed that work-related stress studies frequently discuss 39 key topics, as determined by researchers. Through bibliometric analysis utilizing a network visualization model, it has been determined that there are six recurring clusters of keywords. The initial cluster, highlighted in red, comprises a total of 11 keywords. The terms "job stress" and "job satisfaction" have been the primary focus of research in the field of job stress analysis from 2013 to 2023. In response to the analysis of overlay visualization, the keyword "job stress" has been frequently examined from the past to the present. According to the density visualization analysis, the keywords "job stress" and "job satisfaction" exhibit the most intense and prominent colors, suggesting that numerous researchers have conducted studies on job stress.
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Liwafa, Arizal, Bagong Suyanto, and Zuyyinna Choirunnisa. "Sustainable Competitive Advantage: A Literature Review and Future Research." RSF Conference Series: Business, Management and Social Sciences 3, no. 3 (2023): 428–39. http://dx.doi.org/10.31098/bmss.v3i3.707.
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Sustainable competitive advantage (SCA) is very important for companies in analyzing the quality of human resources and the company's environment that continues to change. Besides that, SCA is also able to provide short-term revenue increases and product advantages to customers. This research aims to map the topic of SCA in various scientific fields by visualizing it into a landscape map to see its development and novelty using bibliographic data. Thus, the novelty of research related to strategic management from previous studies can be known with certainty. This research is quantitative research with a descriptive approach. Documentation and literature studies in this study were obtained through the Scopus database with the keyword "sustainable competitive advantage". Bibliometric techniques were used for analysis and processed using Vosviewer software. The results of this study indicate that from 2017-2023, there are 205 articles related to the keywords used, the results of the Vosviewer analysis show 168 keywords that are connected to the keywords used, keywords that are widely used related to the keywords in this study are SCA, Sustainability, Sustainable Development, Innovation, while keywords that are rarely used are SME, Supply Chain, Competitive Strategy, these rarely used keywords can be used as a choice of future research topics.
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Jung, Young Son. "Research Trends in Unification Education using Keywords Network Analysis." Korean Association For Learner-Centered Curriculum And Instruction 20, no. 8 (2020): 325–35. http://dx.doi.org/10.22251/jlcci.2020.20.8.325.
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Meethan, Kevin. "Keywords in Qualitative Methods: A Vocabulary of Research Concepts." Sociological Research Online 12, no. 1 (2007): 199. http://dx.doi.org/10.1177/136078040701200105.
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Lewison, Grant. "Definition of Cancer Research: Journals, Titles, Abstracts or Keywords?" DESIDOC Journal of Library & Information Technology 31, no. 5 (2011): 333–39. http://dx.doi.org/10.14429/djlit.31.5.1189.
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Kim, Dong-geun, Junghwan Kim, and Hong-Gyu Choi. "Study on Academic Research Keywords Related to K-Dance." Journal of Digital Contents Society 25, no. 11 (2024): 3103–13. https://doi.org/10.9728/dcs.2024.25.11.3103.
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Ramos-Medina, Sonia Elizabeth. "Tax Management Research, Bibliometric Analysis Using Keywords and Abstracts." Journal of Sustainable Development and Green Technology 1, no. 1 (2023): 19–30. http://dx.doi.org/10.54216/jsdgt.010102.
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The challenges derived from tax operations in different jurisdictions make the management of tax obligations increasingly complex. Taking this into account, a bibliometric analysis is proposed to shed light on the content of the publications from a word analysis to identify research topics in a set of publications and establish relationships between concepts. The findings show a dominance of two large clearly segmented clusters: on the one hand, multinational companies trying to better manage their profits; and on the other, the Tax Agency trying to avoid tax evasion at all costs.
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Nagaratnam, Shalini, Ebrahim Nader Ale, and Muzafar Shah Habibullah. "A Bibliometric Analysis on "Fertility Rate" Research Trends." International Journal of Professional Business Review 1, no. 1 (2016): 1–14. https://doi.org/10.5281/zenodo.58318.
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The systematic literature search on the fertility rate was performed on articles from the Web of Science compiled by the Institute of Scientific Information (ISI) to provide major publication characteristics. All articles related to Fertility from year 1980 to 2011 were analyzed with the bibliometric approach using non-parametrics techniques. Total Citation, Citation per year and Cited References were significantly different between the 80’s, 90’s and 2000’s, with more than a 100% increase in the number of publication. The association between number of authors and citation per year were significantly weak. Number of publication in a journal posed a poor negative association with the citation per year. Similarity between Authors Keyword and KeyWords Plus® do not promote the increase in citation per year. The author’s affiliation and country of publication would be an interesting exploration for future fertility research. The bibiliometric analyses on Fertility is rather new and vital, as the key finding will provide researchers guidance in the literature search and future publication.
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Tulloch, Bonnie J., Michelle Kaczmarek, Saguna Shankar, and Lisa P. Nathan. "When words are key: negotiating meaning in information research." Journal of Documentation 80, no. 7 (2024): 187–205. http://dx.doi.org/10.1108/jd-05-2023-0103.
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PurposeThis project set out to explore information scholars’ perceptions of the influence of their keyword selections and the implications of their linguistic choices on possibilities for and perceptions of the field of Information Science. We trialed a narrative methodological approach to investigate the multiple stories told with specific keywords, how they relate to larger discourses within the field and the impact they have on the lives of information researchers.Design/methodology/approachThis paper draws on Arthur Frank’s narrative analysis to consider keywords as stories, which shape one’s sense of professional identity and belonging. The analysis, which is informed by insights from multi-disciplinary scholars of keywords, employs data from a keywords-oriented workshop with Information School faculty and students, as well as an online questionnaire sent to heads of Information Schools.FindingsWe did not find a singular definitive story of information science scholars’ experiences with keywords. Rather we identify tensions surrounding common and contested understandings of discipline, canon and information, engaging the complexity of interdisciplinary, international, intellectual and moral claims of the field. This research offers insight into the experiential factors that shape scholars’ engagement with keywords and the tensions they can create.Originality/valueA wealth of bibliometric analyses of keywords focuses on finding the “right” words to describe the scholarship you seek or the work you want others to discover. However, this study offers information researchers a novel approach, creating space to acknowledge the generative tensions of keywords, beyond the extractive logic of search and retrieval.
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ZHANG, JUNJUAN, and KISOON HAN. "Analysis of Creativity Research Trends in China Using Keyword Network Analysis." Korean Society for Creativity Education 24, no. 1 (2024): 13–39. http://dx.doi.org/10.36358/jce.2024.24.1.13.
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The purpose of this study is to analyze trends in creativity research in China using keyword network analysis. It aims to identify the characteristics of studies related to creativity conducted in China and provide implications for future research in this area. To achieve this, 1680 keywords were extracted from 427 papers, including dissertations and academic journal articles published on the Chinese academic paper site CNKI from 2010 to 2022. After refining the extracted keywords, a keyword matrix was constructed, and the keywords were analyzed using UCINET. The analysis revealed several findings. Firstly, “creativity (創意性)” emerged as the most frequently extracted keyword, followed by “originality (獨創性),” “graphic design (平面設計),” “aesthetics (審美性),” “creative thinking (創意性思維),” “early childhood education (幼兒教育),” “package design (包裝設計),” “design (設計),” “sketch teaching (寫生教學),” and “creative design (創意設計).” Secondly, the entire network comprised a single component with organic connections between keywords, with “creativity” exhibiting high degrees of centrality. Thirdly, to delve into the internal meaning of each subnetwork, the ego network of the top four keywords—'creativity (創意性),' 'packaging design (包裝設計),' 'aesthetics (審美性),' and 'design (設計)'—was analyzed and visualized. Through this macro-level exploration, the study aimed to provide insights into the major trends and topics of research related to creativity in China from 2010 to 2022, offering guidance for subsequent researchers to delve more comprehensively into creativity research trends in China.
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Kim, Hyunjung. "The JASIST Editorial Board Members' Research Areas and Keywords of JASIST Research Articles." Journal of the Korean Society for information Management 31, no. 3 (2014): 227–47. http://dx.doi.org/10.3743/kosim.2014.31.3.227.
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Merriam, Dan. "Key Words or Keywords." Compass: Earth Science Journal of Sigma Gamma Epsilon 84, no. 4 (2013): 6–7. http://dx.doi.org/10.62879/c10884774.
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Keywords are words or phrases which describe content. With the emergence of computerized data bases, keywords are important research tools for search engines, such as GeoRef. Keywords help to ‘tag’ relevant information within an article, and provide a researcher with useful clues to potential important and useful information.
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Sung, Hyeyeon, and Sungkyu Lee. "Analysis on Research Trend of Gaming Addiction using Keyword Network Analysis." Correction Welfare Society of Korea 84 (December 31, 2023): 29–55. http://dx.doi.org/10.35422/cwsk.2023.84.29.
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The purpose of this study is to suggest the direction of future studies on the prevention and treatment of gaming addiction by analyzing the trends in research related to gaming addiction for the last 10 years. To accomplish this purpose, this study analyzed publication trends in research related to gaming addiction by examining 175 studies published between 2013 and 2023. Particularly, a keyword network analysis was conducted using keywords extracted from these studies. The main results are as follows. First, the results of the keyword frequency analysis indicate that the most frequently occurring terms in the 175 publications are, in descending order, ‘adolescent’, ‘gaming addiction, ‘Internet gaming addiction’, ‘depression’, and ‘aggression.’ Second, the results of the TF-IDF analysis demonstrate that keyword weights of 42 keywords were high, listed in the following order: ‘Internet gaming addiction’, ‘gaming addiction’, ‘adolescent’, ‘aggression’, and ‘depression.’ Third, the results of the keyword network centrality analysis indicate that the keywords with high degree centrality, closeness centrality, betweenness centrality were ranked as follows: ‘gaming addiction’, ‘adolescent’, ‘Internet gaming addiction’, ‘depression.’ Based upon these results, this study proposes directions for future research on the prevention and treatment of gaming addiction.
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Cao, Nan. "Research on the Impact of Social Media Keyword-based Recommendation Methods on Users." Lecture Notes in Education Psychology and Public Media 36, no. 1 (2024): 216–25. http://dx.doi.org/10.54254/2753-7048/36/20240463.
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The purpose of this study is to investigate the keyword-based recommendation strategy of social media and the impact of this recommendation strategy on user attitudes. In this study, three representative content creation social media platforms, Weibo, TikTok, and Bilibili, were selected to investigate their recommendation strategies, and a questionnaire was designed to simulate the content recommendation strategies of social media platforms after users input keywords to investigate users' attitudes towards keyword-based content recommendation strategies in social media. Through investigation, the keyword-based recommendation strategy of social media is mainly to recommend content explicitly associated with keywords. After the user returns to the home page, the system occasionally recommends a small amount of content implicitly associated with keywords. After a given keyword, social media users are more willing to click on content closely related to themselves when facing multiple recommended content. After a series of user choices, a series of entries recommended by the system to most users are explicitly associated with a given keyword. The content recommendation strategy with explicit association is still dominant in social media.
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Abdi Khalife, Mehrzad, Anna Dunay, and Csaba Bálint Illés. "Bibliometric Analysis of Articles on Project Management Research." Periodica Polytechnica Social and Management Sciences 29, no. 1 (2020): 70–83. http://dx.doi.org/10.3311/ppso.15717.
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Project management, as a subsidiary of social science, is a vast and varied topic of the area of knowledge. In the past decades, many studies have compiled an immense amount of information for theoreticians and practitioners in this field. In this paper, traditional and novel methods of bibliometric analysis are introduced through a survey for analyzing the history of research in project management. This study focuses on the last four decades of publications on project management, from 1980 to 2019. In the survey, the number of publications, the countries of publication, the cooperating relations among those countries, and the top categories of publications are analyzed. The extraction of publication keywords and the investigation of knowledge seeds are also presented. In the survey, the examination of the network of top occurring keywords, keyword clustering, together with the keyword correlation matrix, were used to explore the main trends in project management. A novel indicator, called the ICCO ranking, is presented by using the degree, betweenness and cluster coefficient of the network of keywords. Using this indicator, the potential knowledge seeds in project management may be identified.
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Lee, HooHee, Bo-Ra Jeon, and Hyoung Jin Won. "Research Trends in Catholic Education through Semantic Network Analysis." Korean Association for the Study of Religious Education 76 (March 31, 2024): 17–38. http://dx.doi.org/10.58601/kjre.2024.03.30.02.
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[Objective] In this study, we conducted an analysis of Catholic education research trends by utilizing multiple academic paper search engines. Through keyword searches such as ‘Catholic,’ ‘education,’ ‘Catholic education,’ and ‘faith education,’ we identified important keywords, centrality, and performed CONCOR analysis among the papers retrieved. This enabled us to gain insights into the research trends in Catholic education. Departing from conventional literature reviews, it aimed to explore the evolution of research in Catholic education, emphasizing the interrelationships among keywords through an objective and scientific method of keyword network analysis.
 [Contents] The analysis revealed that the centrality of major keywords in academic journal articles related to Catholic education included terms such as ‘Catholic Church,’ ‘education,’ ‘catechesis,’ ‘faith,’ and ‘Christ.’ In the TF-IDF analysis indicating keyword importance, terms like ‘religious education,’ ‘youth,’ ‘Korea,’ ‘theology,’ and ‘character’ emerged at the forefront. Finally, the CONCOR analysis related to Catholic education research defined groups such as ‘history and direction of catechesis,’ ‘ethical education of educators and schools,’ ‘holistic education in Catholic schools,’ and ‘practical faith education.’ [Conclusion] Through this study, analyzing the trends in Catholic education is expected to broaden the comprehensive understanding of Catholic education from an integrated perspective. Additionally, it offers suggestions for future directions in Catholic education research through this comprehensive analysis.
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Seo, Bo Soon. "Keywords Network Analysis of Korean Research Trends on Child Abuse." Forum of Public Safety and Culture 20 (January 30, 2023): 159–80. http://dx.doi.org/10.52902/kjsc.2023.20.159.
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The purpose of this study was to visualize the paradigm and flow of child abuse studies in Korea, and to examine the concepts that were important. In this study, by examining trends in child abuse studies through keywords network analysis, the frequency trends and networks of keywords presented in previous studies were reviewed. Through this, I tried to provide implications for children's rights and comparison with other trend analysis researches. For this purpose, 5,813 keywords of 450 types from 1,203 research articles published from 2001 to 2022 were selected and analyzed using the UCINET6 program and the NetDraw program. As a result of the study, first, 'child', 'abuse', 'adolescent', 'parent', 'abuse experience', 'neglect', and 'depression' frequently appeared as the keywords in child abuse research. And, there was a slight difference in the frequency trend depending on the time period. Second, the entire network had a structure including 448 nodes, 7,588 links, and 2 components. Third, the keywords 'adolescence', 'parent', 'abuse experience', 'neglect', 'depression', 'emotional abuse', 'sexual abuse', and 'physical abuse' were keywords with high centrality. Fourth, the ego network of ‘adolescence’, ‘parent’, ‘abuse experience’, and ‘neglect’ had high connection strength between the four words, and was also strongly bound with ‘misconduct’, ‘depression’, and ‘school adjustment’. And the 'adolescence' ego network was connected to 'runaway', 'peer', 'suicidal ideation', 'suicide', and 'family', and the 'parent' ego network was connected to 'children', 'relationship', 'attachment', ' teacher', 'parenting attitude', and 'recognition' were connected. The 'abuse experience' ego network is connected to 'suicide', 'emotion', 'school violence', 'social support', and 'art therapy', and the 'neglect' ego network is linked to 'interpersonal relationships', 'teacher', ' post-traumatic stress' and 'ego-resilience' were strongly linked. Like previous studies, this study also found that the number of child abuse studies had increased since the middle of 2010, and there was a focus on exploring influencing variables. In addition to this, in this study, important keywords that penetrate the period were derived from child abuse studies, and a network of concepts connected to the keywords was identified. In addition, by discovering the focus of studies by period, it provided implications for future research and policy implementation on child abuse.
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Kim, Kyeong-Ju, and Il-Bang Kim. "Research Trends of a Doctoral Dissertation Related to ‘Jeju Education’." Society for Jeju Studies 60 (August 31, 2023): 263–92. http://dx.doi.org/10.47520/jjs.2023.60.263.
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This study identified the trend of Jeju education-related research targeting doctoral
 dissertation. Doctoral dissertation were collected from ‘RISS’, ‘National Library of Korea’
 and ‘The National Assembly Library’, 70 papers were selected by reviewing whether or
 not they were related to Jeju education. And For keyword analysis, 466 keywords were
 derived and analyzed. The research results are as follows. 1) The first doctoral
 dissertation related to Jeju education was published in 1990. And since 2012, the
 frequency of dissertations publication has increased. From 1990 to 2007, 10
 dissertations were published outside of Jeju. Since 2008, dissertations has been
 published at Jeju National University. Of the total 60 dissertations published since 2008,
 48(80%) dissertations are published by Jeju National University. Doctoral dissertations
 are mostly related to science education and computer education. 2) The network
 attribute has low density between keywords. And the cluster coefficient is high. The
 structure of the network consists of ① keywords related to education programs, ②
 keywords related to humanities, arts, society, and nature in Jeju, which is expressed as
 'Jeju type' ③ keywords related to students & teacher ④ keywords related to curriculum
 and subject content, ⑤ keywords related to educational institutions and policies. Finally,
 based on the research results, the implications and directions of research related to Jeju
 education were suggested.
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Nasrum, Akbar, and Agus Nasir. "Chamilo LMS Research Trend." International Journal of Business, Law, and Education 4, no. 2 (2023): 730–37. http://dx.doi.org/10.56442/ijble.v4i2.233.
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Chamilo is an open-source Learning Management System that is rarely known by campus communities in Indonesia, even though this LMS has been published for quite some time. Very little research is related to Chamilo compared to other LMS such as Moodle. This study will examine research trends over the past ten years using the Systematic Literature Review method. The subjects studied were taken from the Dimension and Scopus databases. The tools used are VoS Viewer and Mendeley software. The unit of analysis used is the "co-author," which is studied deductively. In addition, the type of "co-occurrence" is also used to see the keywords that are most often used in research. The research results showed that 69% of all articles were from Indonesia. The type of development research also dominates articles originating from Indonesia. The most dominant keyword besides Chamilo itself is e-learning. Several research keywords that are not included in the occurrence analysis unit have the potential to be studied.
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Shin, Matthew Minsuk, Seunghye Jung, and Jin Sung Rha. "Study on Business Ecosystem Research Trend Using Network Text Analysis." Sustainability 13, no. 19 (2021): 10727. http://dx.doi.org/10.3390/su131910727.
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The management environment is moving into a new phase with the changing global circumstances. The business ecosystem as a management strategy has been studied for the last 30 years since the concept was introduced. The purpose of this study was to analyze the research trend in business ecosystem by using network next analysis and to understand the concept, being one that is still being actively studied. Network text analysis is a commonly used method to analyze research trends by forming networks based on bibliographic data of the articles, namely, keywords. For the analysis, we collected the data and keywords from 340 research papers published in global academic journals related to business ecosystem on the basis of the Scopus database. Through keywords extraction and cleansing, we found that the keywords of “innovation”, “sustainability”, and “platform” were mentioned most frequently, and the research topics were correlated to each other. Moreover, we conducted degree centrality and betweenness centrality analysis along with clustering analysis by transforming the two-mode network into a one-mode network. Degree centrality involves analyzing the degree to which one keyword links to other keywords, and betweenness centrality shows the mediating effects of a keyword to other keywords. In the centrality analysis results, “innovation”, “sustainability”, “platform”, and “business model” showed the highest degree centrality, and “sustainability”, “innovation”, “China”, and “platform” had the highest betweenness centrality. Then, we classified the clusters of subtopics into five groups. The current study examined accumulated research and suggested a comprehensive understanding of the research trend in business ecosystem by incorporating a method enabling research trend analysis to secure objectivity. This research is expected to help researchers to review the research trend in business ecosystem and identify expandable topics for further studies.
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Kim, Kyoung Eun. "An Analysis of Research Trends on Creative Teaching." Korean Association For Learner-Centered Curriculum And Instruction 22, no. 12 (2022): 535–53. http://dx.doi.org/10.22251/jlcci.2022.22.12.535.
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Objectives The purpose of this study was to examine the studies on creative teaching published in Korean journals through the content analysis method and keyword network methods.
 Methods TThe final 77 papers among the papers searched as keywords for ‘creative teaching’ in Korean journals were targeted. For content analysis, the 77 papers were analyzed in terms of topics and research methods using an analysis frame. In addition, 216 keywords were derived for keyword network analysis, and frequency analysis and centrality analysis were conducted using textom programs and UCINET.
 Results First, before 2008 the main topic of creative teaching was creative teaching methods. From 2009 to 2014, in addition to creative teaching methods, various concepts such as creative teaching behavior, creative teaching beliefs, and creative teaching environment began to emerge, and after 2015, research on creative teaching behavior increased, and new concepts such as creative teaching competency and creative teaching efficacy were introduced. Also, from 2009 to 2104, most studies on creative teaching were conducted at universities, but after 2015, studies on creative teaching at universities and early childhood institutions increased. Second, the main keywords of creative teaching research were ‘teaching method’, ‘creative problem solving’, ‘child creativity’, and ‘teacher creativity’. As a result of network analysis, ‘creative teaching behavior’, ‘creativity’, and ‘creative teaching method’ were dominant keywords in degree centrality, closeness centrality, and mediation centrality.
 Conclusions This study reviewed systematically research on creative teaching by period, and explored the relationship between the variables related to creative teaching. Implications for the results and future studies were discussed.
 
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Kim, Kyoung Eun. "An Analysis of Research Trends on Creative Teaching." Korean Association For Learner-Centered Curriculum And Instruction 22, no. 12 (2022): 547–65. http://dx.doi.org/10.22251/jlcci.2022.22.12.547.
Full textAbstract:
Objectives The purpose of this study was to examine the studies on creative teaching published in Korean journals through the content analysis method and keyword network methods.
 Methods TThe final 77 papers among the papers searched as keywords for ‘creative teaching’ in Korean journals were targeted. For content analysis, the 77 papers were analyzed in terms of topics and research methods using an analysis frame. In addition, 216 keywords were derived for keyword network analysis, and frequency analysis and centrality analysis were conducted using textom programs and UCINET.
 Results First, before 2008 the main topic of creative teaching was creative teaching methods. From 2009 to 2014, in addition to creative teaching methods, various concepts such as creative teaching behavior, creative teaching beliefs, and creative teaching environment began to emerge, and after 2015, research on creative teaching behavior increased, and new concepts such as creative teaching competency and creative teaching efficacy were introduced. Also, from 2009 to 2104, most studies on creative teaching were conducted at universities, but after 2015, studies on creative teaching at universities and early childhood institutions increased. Second, the main keywords of creative teaching research were ‘teaching method’, ‘creative problem solving’, ‘child creativity’, and ‘teacher creativity’. As a result of network analysis, ‘creative teaching behavior’, ‘creativity’, and ‘creative teaching method’ were dominant keywords in degree centrality, closeness centrality, and mediation centrality.
 Conclusions This study reviewed systematically research on creative teaching by period, and explored the relationship between the variables related to creative teaching. Implications for the results and future studies were discussed.
 
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Huang, Yue, Hu Liu, and Jing Pan. "Identification of data mining research frontier based on conference papers." International Journal of Crowd Science 5, no. 2 (2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.
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Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
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Khan, Noel, David Elizondo, Lipika Deka, and Miguel A. Molina-Cabello. "Natural Language Processing Tools and Workflows for Improving Research Processes." Applied Sciences 14, no. 24 (2024): 11731. https://doi.org/10.3390/app142411731.
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The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results.
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Han, Yoo Jin, and Soo Kyeong Woo. "Research Trends of the Infant and Young Child Observation using Keyword Network Analysis." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 15 (2023): 793–813. http://dx.doi.org/10.22251/jlcci.2023.23.15.793.
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Objectives This study analyzed research trends of the infant and young child observation using keyword network analysis.
 Methods To this end, keywords were extracted from 71 academic papers on the infant and young child observation which were preserved in the Korea Educational Information Service Database (RISS) under the theme of “infant-young children (infant, young children) + observation.” After refining the collected keywords, 161 keywords were analyzed by UCINET 6, and then visualized using Netdraw.
 Results First, in research related to infant and young child observation, “observation,” “infants,” “pre-service teachers,” “rating scales,” “behavior,” “recording,” “play,” and “research” appeared as major keywords with high frequency of appearance. Visualized results showed that “observation,” “pre-service teachers,” “infants,” and “recording” were complicatedly correlated. This results also showed that all the keywords including “observation,” “infants,” “rating scales,” “pre-service teachers,” “recording,” and “behavior” had high levels of degree centrality, closeness centrality, and betweenness centrality. Second, results of examining the keywords derived from the infant and young child observation studies over the past 30 years revealed that major keywords of three periods were as follows: The first period (1994-2011) - “behavior,” “rating scales,” “development,” the second period (2012-2018) - “preservice teachers,” “rating scales,” and “music activities,” and the third period (2019-2023) - “recording,” “play observation,” and “observation capabilities.”
 Conclusions This study is meaningful in that it investigated areas, connectedness, and periodical emphases of the infant and young child observation studies using keyword network analysis and sought ways of diversifying future research directions. It would be necessary to seek ways to support educational practice in the day-care centers and kindergartens through actively conducting the infant and young child observation.