• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2529-2543
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• 2019

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.158-159
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• 2017

Sentiment analysis is the review of written or verbal communications to determine some measure of emotion or feeling in the communication. Search engines are one of the most popular sites visited on the Internet generating hundreds of billions of hits per month worldwide. Obviously privacy policies related to these search sites are extremely important. Our study reviews the privacy policies of the two largest search engines, Google and Baidu to determine the overall sentiment of their privacy policies. Significant individual findings and significant differences were found using several sentiment and opinion analysis methods.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.25-31
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• 2015

General student orchestra activity is known as desirable hobby for students of adolescent period, developing their creativity and sensitivity, give students sense of belonging and stability by resolving their social, emotional anxiety. Accordingly, his research investigated whether orchestra club activity also has similar effect on university students. As a result unlike of adolescent students, orchestra activity turned out to be not that helpful for the social, self-confidence improvement of university students, though achievement of the activity itself was high. Despite of the result, there exist positive factors; obviously the activity has positive factors analyzing through recognition analysis (opinion mining) using big data. Therefore national support is required also for the orchestra activity of the undergraduates.

• Journal of KIISE
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• v.42 no.4
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• pp.512-521
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• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.37-48
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• 2022

With development of Digital Technology, social issues are communicated through digital-based platform such as SNS and form public opinion. This study attempted to analyze big data from Twitter, a world-renowned social network service, and find out the public opinion. After collecting Twitter data based on 14 keywords for 1 year in 2021, analyzed the term-frequency and relationship among keyword documents with pearson correlation coefficient using Data-mining Technology. Furthermore, the 6 main topics that on the center of information security field in 2021 were derived through topic modeling using the LDA(Latent Dirichlet Allocation) technique. These results are expected to be used as basic data especially finding key agenda when establishing strategies for the next step related industries or establishing government policies.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.76-82
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• 2020

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.201-210
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• 2024

In empirical software engineering research, there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Overall, they consider the whole survey population as a single group with some sampling techniques to extract varieties. In some cases, the population is also partitioned into sub-groups based on some background information. However, this does not reveal opinion diversity properly as similar opinions can exist in different segments of the population, whereas people within the same group might have different opinions. Even though existing approach can capture the general trends there is a risk that the opinions of different sub-groups are lost. The problem becomes more complex in case of longitudinal studies where minority opinions might fade or resolute over time. Survey based longitudinal data may have some potential patterns which can be extracted through a clustering process. It may reveal new information and attract attention to alternative perspectives. We suggest using a data mining approach to finding the diversity among the different groups in longitudinal studies (agile skeptics). In our study, we show that diversity can be revealed and tracked over time with the use of clustering approach, and the minorities have an opportunity to be heard.