• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.63-88
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• 2018

This research evaluated differences of classification performance for feature selection methods using LDA topic model and Doc2Vec which is based on word embedding using deep learning, feature corpus sizes and classification algorithms. In addition to find the feature corpus with high performance of classification, an experiment was conducted using feature corpus was composed differently according to the location of the document and by adjusting the size of the feature corpus. Conclusionally, in the experiments using deep learning evaluate training frequency and specifically considered information for context inference. This study constructed biomedical document dataset, Disease-35083 which consisted biomedical scholarly documents provided by PMC and categorized by the disease category. Throughout the study this research verifies which type and size of feature corpus produces the highest performance and, also suggests some feature corpus which carry an extensibility to specific feature by displaying efficiency during the training time. Additionally, this research compares the differences between deep learning and existing method and suggests an appropriate method by classification environment.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.316-330
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• 2020

The study analyzes reviews of hardware products, customer service products, and products that take the form of a convergence of hardware and cloud services in ICT using text mining. We derive keywords of each review and find the differentiation of words that are used to derive topics. A cluster analysis is performed to categorize reviews into their respective clusters. Through this study, we observed which keywords are most often used for each product type and found topics that express the characteristics of products and services using topic modeling. We derived keywords such as "professional" and "technician" which are topics that suggest the excellence of the service provider in the review of service products. Further, we identified adjectives with positive connotations such as "favorite", "fine", "fun", "nice", "smart", "unlimited", and "useful" from Amazon Eco review, an integrated product and service. Using the cluster analysis, the entire review was clustered into three groups, and three product type reviews exclusively resulted in belonging to each different cluster. The study analyzed the differences whereby consumer needs are expressed differently in reviews depending on the type of product and suggested that it is necessary to differentiate product planning and marketing promotion according to the product type in practice.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.352-367
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• 2020

This study critically reviewed whether the South Korean press reported the issue of conflict between South Korea and Japan in a way consistent with their journalistic values. To this end, this study conducted frame analysis and textual analysis for the articles of three press(Chosun Ilbo, Hankyoreh and KBS) from the three major branches of conflict (Korea's Supreme Court's ruling on forced labor compensation in October 2018, the Japanese government's decision to regulate exports in July 2019 and the Korean government's decision to end GSOMIA in August 2019) to one week. There were many superficial reports of simply relaying conflicts around the occurrence and outcome of events, and there were few reports that analyzed the context in depth or suggested alternatives. And partisan reporting, which is cited as a key issue in the Korean journalism, has been strongly revealed in the midst of a conflict between Korea and Japan, a national emergency situation.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.617-624
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• 2016

The hangul word embedding should be performed certainly process for noun extraction. Otherwise, it should be trained words that are not necessary, and it can not be derived efficient embedding results. In this paper, we propose model that can retrieve more efficiently by query language expansion using hangul word embedded, apriori, and text mining. The word embedding and apriori is a step expanding query language by extracting association words according to meaning and context for query language. The hangul text mining is a step of extracting similar answer and responding to the user using noun extraction, TF-IDF, and cosine similarity. The proposed model can improve accuracy of answer by learning the answer of specific domain and expanding high correlation query language. As future research, it needs to extract more correlation query language by analysis of user queries stored in database.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.1
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• pp.25-35
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• 2020

Since the introduction and operation of BF certification, a total of 6,432 certificates has been issued until February 2020, of which educational research facilities make 1,091 cases (754 preliminary certification, 337 main certification) out of 6,237 buildings, acquiring BF certification of about 20%. Qualitative evaluation is conducted with focus on the three items of BF-certified building evaluation index, which are medium facilities, internal facilities, and sanitary facilities, and major keywords are the deducted through the Text Mining analysis of the derived results. As a result, problems with access paths occurred in the case of the facilities, and assessment indicators for users were found to be necessary among the assessment of the steps of the internal facilities. Finally, we could see that sanitation facilities needed to improve toilets installed in residential development facilities. Based on the results obtained, the study seeks to suggest directions for improving the evaluation index required for BF-certified school facilities.

• Journal of Korean Academy of Nursing Administration
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• v.23 no.1
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• pp.101-110
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• 2017

Purpose: This study aimed to identify research trends in the Journal of Korean Academy of Nursing Administration from 2013 to 2015. Methods: For this study, 171 articles were analyzed. Research designs, participants, research settings, sampling, and data analyses methods were reviewed using established analysis criteria. Keyword centrality and clusters were generated by keyword network analysis. Results: Most of studies used quantitative methods (82.5%), and sampling mainly focused on nurses (68.8%). The most commonly used data analyses methods were t-test, ANOVA, correlation, and regression. The most central keywords were turnover and empowerment. Network analysis generated four network groups: 1) burnout; 2) turnover; 3) happiness; and 4) nursing professionalism. Conclusion: The results of this study identify current trends and interests in Korean nursing administration research. The findings from this study suggest that future studies include a variety of research methods and maintain appropriate research ethics.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.317-324
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• 2012

Recently, coordination task of similarity between national R&D programs is emphasized on view from the R&D investment efficiency. But the previous similarity search method like text-based similarity search which using keyword of R&D projects has reached the limit due to deviation of document's quality. For the solve the limitations of text-based similarity search using the keyword extraction, in this study, utilization of R&D project's technical classification will be discussed as a new similarity search method when analyzed of similarity between national R&D programs. To this end, extracts the Science and Technology Standard Classification of R & D projects which are collected when national R&D Survey & analysis, and creates peculiar vector model of each R&D programs. Verify a reliability of this study by calculate the cosine-based and Euclidean distance-based similarity and compare with calculated the text-based similarity.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.33-51
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• 2003

The primary objectives of this study were, using data from Internet users in Korea, to determine users' preference of banner ad through two ad properties; ad presentation type (text vs. image) and ad content (product information vs. prize information) by incorporating the level of involvement into research design. Using within-group experimental design by means of subjects' web-based participation in the study, the study result showed that image-based banner ad was significantly preferred to text-based banner ad. It was found that the level of ad involvement had a significant impact on the preference of banner ads. Also it was found that image-based banner ad had a greater effect on ad preference than text-based banner ad in low involvement situation only, Finally, image-based banner ad was consistently preferred to text-based banner ad regardless of involvement level when the banner ad was product oriented. The study findings suggest that adoption decisions regarding banner ad presentation type and banner ad content should be based on the knowledge of both the level of consumer's ad involvement and the interactive effects between ad presentation and ad content.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.173-182
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• 2016

AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.