• Journal of the Korea society of information convergence
• /
• v.7 no.2
• /
• pp.49-57
• /
• 2014

Recently, opinion mining is introduced to extract useful information from SNS data and to evaluate the true intention of users. Opinion mining are required several efficient techniques to collect and analyze a large amount of SNS data and extract meaningful data from them. Therefore in this paper, we propose a parallel HDFS(Hadoop Distributed File System) and emotion functions based on Mapreduce to extract some emotional information of users from various unstructured big data on social networks. The experiment results have verified that the proposed system and functions perform faster than O(n) for data gathering time and loading time, and maintain stable load balancing for memory and CPU resources.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.540-543
• /
• 2017

It will be possible to solve some of the major issues in our society and economy with the emerging Big Data used across 21st century global digital economy. One of the main areas where big data can be quite useful is the medical and health area. IT technology is being used extensively in this area and expected to expand its application field further. However, there is still room for improvement in the usage of Big Data as it is difficult to search unstructured data contained in Big Data and collect statistics for them. This limits wider application of Big Data. Depending on data collection and analysis method, the results from a Big Data can be varied. Some of them could be positive or negative so that it is essential that Big Data should be handled adequately and appropriately adapting to a purpose. Therefore, a Big Data has been constructed in this study to applying Crawling technique for data mining and analyzed with R. Also, the data were visualized for easier recognition and this was effective in developing an individualized health plan from different angles.

• Journal of Fisheries and Marine Sciences Education
• /
• v.24 no.4
• /
• pp.494-506
• /
• 2012

The purpose of this study was to analyse 3, 4, 5-year-old children's private speech according to their age and task characteristics (structured task vs. unstructured task). In order to achieve the goal, the main effect of age, characteristic of tasks and interaction effect were considered among age and characteristic of tasks on preschool children's private speech. The subjects were each 30 3, 4, 5-year-olds from preschool in Busan, South Korea. The structured task was puzzle task and the unstructured task was drawing task from TCT-DP. The data was analyzed by repeated measurement two way ANOVA: 3(age) ${\times}$ 2((characteristic of task). As a result, firstly, total private speech of 4-year-old was higher than 3-year-old, 5-year-old in both tasks, and total private speech of 5-year-old was higher than 3-year-old in both tasks. Secondly, the task-irrelevant private speech was not affected by main effect of age and characteristic of task and interaction effect between age and characteristic of task. Thirdly, the task-relevant private speech was received both main effects and interaction effects between age and characteristic of task. Finally, the external manifestation of inner speech were not received effect of age but received effect of characteristic of task, and received interaction effect between age and characteristic of task. The results of this study imply that characteristic of task is an important factor inducing children's private speech.

• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.73-83
• /
• 2019

Since the emergence of the fourth industrial revolution, data analysis is being conducted in various fields. Distributed data processing has already become essential for the fast processing of large amounts of data. However, in the defense sector, simulation used cannot fully utilize the unstructured data which are prevailing at real environments. In this study, we propose a distributed data processing platform that can be applied to battalion level simulation models to provide visualized data for command decisions during training. 500,000 data points of strategic game were analyzed. Considering the winning factors in the data, variance processing was conducted to analyze the data for the top 10% teams. With the increase in the number of nodes, the model becomes scalable.

• Journal of Digital Contents Society
• /
• v.18 no.2
• /
• pp.339-346
• /
• 2017

There is increasing interest in text analysis based on unstructured data such as articles and comments, questions and answers. This is because they can be used to identify, evaluate, predict, and recommend features from unstructured text data, which is the opinion of people. The same holds true for TEL, where the MOOC service has evolved to automate debating, questioning and answering services based on the teaching-learning support system in order to generate question topics and to automatically classify the topics relevant to new questions based on question and answer data accumulated in the system. Therefore, in this study, we propose topic modeling using LDA to automatically classify new query topics. The proposed method enables the generation of a dictionary of question topics and the automatic classification of topics relevant to new questions. Experimentation showed high automatic classification of over 0.7 in some queries. The more new queries were included in the various topics, the better the automatic classification results.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.4
• /
• pp.45-55
• /
• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• International Journal of Advanced Culture Technology
• /
• v.10 no.4
• /
• pp.230-237
• /
• 2022

The Community Social Service Investment project started as a state subsidy project in 2007 and has grown very rapidly in quantitative terms in a short period of time. It is a bottom-up project that discovers the welfare needs of people and plans and provides services suitable for them. The purpose of this study is to analyze using big data to determine the social response to local community service investment projects. For this, data was collected and analyzed by crawling with a specific keyword of community service investment project on Google and Naver sites. As for the analysis contents, monthly search volume, related keywords, monthly search volume, search rate by age, and gender search rate were conducted. As a result, 10 items were found as related keywords in Google, and 3 items were found in Naver. The overall results of Google and Naver sites were slightly different, but they increased and decreased at almost the same time. Therefore, it can be seen that the community service investment project continues to attract users' interest.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.33-56
• /
• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.95-110
• /
• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Korean Journal of Computational Design and Engineering
• /
• v.1 no.1
• /
• pp.56-64
• /
• 1996

A quadrilateral-triangular mixed grid method for the solution of incompressible viscous flow is presented. The solution domain near the body surface is meshed using elliptic grid geneator to acculately simulate the viscous flow. On the other hand, we used unstructured triangular grid system generated by advancing front technique of a simple automatic grid generation algorithm in the rest of the computational domain. The present method thus is capable of not only handling complex geometries but providing accurate solutions near body surface. The numerical technique adopted here is PISO type finite element method which was developed by the present author. Investigations have been made of two-dimensional unsteady flow of Re=550 past a circular cylinder. In the case of use of the unstructured grid only, there exists a considerable amount of difference with the existing results in drag coefficient and vorticity at the cylinder surface; this may be because of the lack of the grid clustering to the surface that is a inevitable requirement to resolve the viscous flow. However, numerical results on the mixed grid show good agreements with the earlier computations and experimental data.