• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.45-66
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• 2018

Crowdfunding has become more popular than angel funding for fundraising by venture companies. Identification of success factors may be useful for fundraisers and investors to make decisions related to crowdfunding projects and predict a priori whether they will be successful or not. Recent studies have suggested several numeric factors, such as project goals and the number of associated SNS, studying how these affect the success of crowdfunding campaigns. However, prediction of the success of crowdfunding campaigns via non-numeric and unstructured data is not yet possible, especially through analysis of structural characteristics of documents introducing projects in need of funding. Analysis of these documents is promising because they are open and inexpensive to obtain. We propose a novel method to predict the success of a crowdfunding project based on the introductory text. To test the performance of the proposed method, in our study, texts related to 1,980 actual crowdfunding projects were collected and empirically analyzed. From the text data set, the following details about the projects were collected: category, number of replies, funding goal, fundraising method, reward, number of SNS followers, number of images and videos, and miscellaneous numeric data. These factors were identified as significant input features to be used in classification algorithms. The results suggest that the proposed method outperforms other recently proposed, non-text-based methods in terms of accuracy, F-score, and elapsed time.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.25-34
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• 2020

Community question and answering service is one of the main sources of information and knowledge in the Web. The quality of information in question and answer documents is determined by the clarity of the question and the relevance of the answers, and the readability of a document is a key factor for evaluating the quality. This study is to measure the quality of documents used in community question and answering service. For this purpose, we compare the frequency of occurrence by vocabulary level used in community documents and measure the readability index of documents by institution of author. To measure the readability index, we used the Dale-Chall formula which is calculated by vocabulary level and sentence length. The results show that the vocabulary used in the answers is more difficult than in the questions and the sentence length is longer. The gap in readability between questions and answers is also found by writing institution. The results of this study can be used as basic data for improving online counseling services.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.17-29
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• 2005

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within a Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order teaming performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.92-103
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• 2003

Great many advertisements are serviced in on-line by development of electronic commerce and internet user's rapid increase recently. However, this advertisement service is stopping in one-side service of relevant advertisement rather than doing users' inclination analysis to basis. Therefore, want advertisement service that many websites are personalized for efficient service of relevant advertisement and service through relevant server's log analysis research and enforce. Take advantage of log data of local system that this treatise is not analysis of server log data and analyze user's Preference degree and inclination. Also, try to propose advertisement system personalized by making relevant site tributary category and give weight of relevant tributary. User's preference user preference which analysis is one part of cooperation fielder ring of web personalized techniques use information in visit site tributary and suppose internet user's action in visit number of times of relevant site and try inclination analysis of mixing form. Express user's preference degree by vector, and inclination analysis result uninterrupted data that simplicity application form is not regarded and techniques that propose inclination analysis change of data since with move data use and analyze newly and proposed so that can do continuous renewal and application as feedback Sikkim. Presented method that can choose advertisements of relevant tributary through this result and provide personalized advertisement service by applying process such as user inclination analysis in advertisement chosen.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.385-389
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• 2020

The medical drama has developed into a story centered on 'people', raising viewers' sympathy. The story of the drama is the true life story of doctors, patients and families. It is also a story that reminds me of 'a little special day of our ordinary people'. And the song played and sung by five characters in the drama became a factor that stimulates nostalgia and increases immersion. The highest viewer rating was 14.1%, and 51,584 blogs alone were registered. According to the big data analysis, the related words were 'Wise OST', 'Album Name', 'Artist Name', 'Two Hours in a row', 'Record', 'Remake', 'OST Revealed', 'Advertisement Revenue', 'Playlist', 'Aroha' and 'Cho Jung-seok'. The commercialization of medical dramas includes 'Sales of Drama OST Albums', 'Organizing Online Live Concerts (PPL in Advertising)', 'Publishing Piano Music', 'Picture of People-Oriented Photography', 'Making Music Video Editing Drama Highlight', 'YouTube Upload Profits', 'Mask' and 'Disinfectant'. it is predicted that the touching story of Corona 19 and the charming humanity will unfold. The limitations of the research will require analysis of various works by genre and attempts to analyze consumer values by industry.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.92-104
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• 2021

This study aims to analyze online news articles to identify social issues related to patient safety and compare the changes in these issues before and after the implementation of the Patient Safety Act. This study performed text mining through the R program, wherein 7,600 online news articles were collected from January 1, 2010, to March 5, 2020, and examined using keyword analysis, topic modeling, and word co-occurrence network analysis. A total of 2,609 keywords were categorized into 8 topics: "medical practice", "medical personnel", "infection and facilities", "comprehensive nursing service", "medicine and medical supplies", "system development and establishment for improvement", "Patient Safety Act" and "healthcare accreditation". The study revealed that keywords such as "patient safety awareness", "infection control" and "healthcare accreditation" appeared before the implementation of the Patient Safety Act. Meanwhile, keywords such as "patient safety culture". and "administration and injection" appeared after the act's implementation with improved ranking of importance pertaining to nursing-related terminology. Interest in patient safety has increased in the medical community as well as among the public. In particular, nursing plays an important role in improving patient safety. Therefore, the recognition of patient safety as a core competency of nursing and the persistent education of the public are vital and inevitable.

• 한국노년학
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• v.41 no.4
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• pp.505-525
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• 2021

Purpose. The purpose of this study is to examine how the phrase "old person" are expressed and used in the online sphere. Based on the theoretical concept of stigma, this study investigates the images and attitudes in society toward the elderly, and the characteristics of hate speech aimed at the elderly. Method. This study conducted text mining based on social big data using anonymous conversations. Results. It was confirmed that the elderly images shared online were generally negative. The attitudes expressed toward them also tended to be negative due to the negative images that are propagated of the elderly. The hate speech relating to the elderly, in usages such as 'Teul-ttag' and 'Kon-dae', were mainly identified in comments that negatively evaluate the elderly, and these expressions demonstrate the depth of hate and discrimination towards the elderly who are considered burdensome by young people. Interestingly, the hateful expressions towards the elderly were found more with regard to issues related to politics and economics and not just any content about the elderly. Conclusions. This study discussed the ways and means to enhance inter-generational understanding and solidity.

• Informatization Policy
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• v.23 no.4
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• pp.76-94
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• 2016

Recently in Korea, the importance of information security awareness has been receiving a growing attention. Attacks such as social engineering and ransomware are hard to be prevented because it cannot be solved by information security technology. Also, the profitability of information security industry has been decreasing for years. Therefore, many companies try to find a new growth-engine and an entry to the foreign market. The main purpose of this paper is to draw out some information security issues and to analyze them. Finally, this study identifies issues and suggests how to improve the situation in Korea. For this, topic modeling analysis has been used to find information security issues of each country. Moreover, the score of sentiment analysis has been used to compare them. The study is exploring and explaining what critical issues are and how to improve the situation based on the identified issues of the Korean information security industry. Also, this study is also demonstrating how text mining can be applied to the context of information security awareness. From a pragmatic perspective, the study has the implications for information security enterprises. This study is expected to provide a new and realistic method for analyzing domestic and foreign issues using the analysis of real data of the Twitter API.