Strategies for the Development of Watermelon Industry Using Unstructured Big Data Analysis
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- The Journal of Industrial Distribution & Business
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- v.12 no.1
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- pp.47-62
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- 2021
Purpose: Our purpose in this study was to examine the strategies for the development of watermelon industry using unstructured big data analysis. That is, this study was to look the change of issues and consumer's perception about watermelon using big data and social network analysis and to investigate ways to strengthen the competitiveness of watermelon industry based on that. Methodology: For this purpose, the data was collected from Naver (blog, news) and Daum (blog, news) by TEXTOM 4.5 and the analysis period was set from 2015 to 2016 and from 2017-2018 and from 2019-2020 in order to understand change of issues and consumer's perception about watermelon or watermelon industry. For the data analysis, TEXTOM 4.5 was used to conduct key word frequency analysis, word cloud analysis and extraction of metrics data. UCINET 6.0 and NetDraw function of UCINET 6.0 were utilized to find the connection structure of words and to visualize the network relations, and to make a cluster of words. Results: The keywords related to the watermelon extracted such as 'the stalk end of a watermelon', 'E-mart', 'Haman', 'Gochang', and 'Lotte Mart' (news: 015-2016), 'apple watermelon', 'Haman', 'E-mart', 'Gochang', and' Mudeungsan watermelon' (news: 2017-2018), 'E-mart', 'apple watermelon', 'household', 'chobok', and 'donation' (news: 2019-2020), 'watermelon salad', 'taste', 'the heat', 'baby', and 'effect' (blog: 2015-2016), 'taste', 'watermelon juice', 'method', 'watermelon salad', and 'baby' (blog: 2017-2018), 'taste', 'effect', 'watermelon juice', 'method', and 'apple watermelon' (blog: 2019-2020) and the results from frequency and TF-IDF analysis presented. And in CONCOR analysis, appeared as four types, respectively. Conclusions: Based on the results, the authors discussed the strategies and policies for boosting the watermelon industry and limitations of this study and future research directions. The results of this study will help prioritize strategies and policies for boosting the consumption of the watermelon and contribute to improving the competitiveness of watermelon industry in Korea. Also, it is expected that this study will be used as a very important basis for agricultural big data studies to be conducted in the future and this study will offer watermelon producers and policy-makers practical points helpful in crafting tailor-made marketing strategies.
The aim of this study is to identify future-oriented tasks for Korean dental hygienists based on a literature review. A literature search is performed using 14 keywords related to tasks carried out by dental hygienists, and included articles published from 2000 to 2019 in the databases KISS, RISS, DBpia, NDSL, Papersearch, Pubmed, and Google Scholar. Six reviewers assessed the titles and abstracts of articles, and an article was selected if the study was considered to cover future-oriented tasks for Korean dental hygienists. Based on the results six local studies and two foreign studies were used for literature review and data extraction. In total, 38 tasks were classified as future-oriented tasks of dental hygienists according to the following criteria: 1) tasks that were specifically referred to as future-oriented tasks, and 2) tasks that could be classified as future-oriented tasks although no specific reference was made. Of these, the most frequently mentioned tasks were measuring periodontal pocket depth, dental hygiene assessment, providing dietary advice, infiltration anesthesia, and root planing. These were extracted from five of the eight studies, including both local and foreign studies. Dental hygiene planning, emergency, emergency management, and smoking cessation were the next most common tasks based on four studies. Even though some of these future-oriented tasks for Korean dental hygienists are included in the dentistry curriculum, and are currently performed as clinical practice for dental hygienists. Nonetheless, the reference to the legal scope is unclear. It is necessary to reconsider the scope of tasks of dental hygienists to reflect changes in domestic and foreign dental care delivery, thereby contributing to the oral health promotion of the public, where safety is guaranteed under legal protection.
This study was analyzed by applying AMSTAR-2, a methodological quality evaluation tool, to evaluate the quality of domestic meta-analysis papers on rehabilitation interventions for stroke patients. The purpose of this study is to provide guidelines for qualitative improvement of evidence-based practice and meta-analysis research by analyzing the qualitative level of the analyzed research. The literature search was conducted using the Research Information Sharing Service, Korean Medical database, and Korean studies Information Service System. Two authors searched, extracted, and reviewed literature using the keywords 'stroke' and 'meta-analysis'. As a result of the AMSTAR-2 quality evaluation of the final 18 studies, 3 studies (16.67%) were 'Moderate', 8 studies (44.44%) were 'Low', and 7 studies (38.89%) were 'Critically Low'. In future research, scientific and objective data selection and extraction process should be performed. It is expected that interest and efforts to improve the quality of meta-analysis research will continue by referring to the contents analyzed in this study as a way to improve the quality of literature.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (