• Journal of Information Technology Applications and Management
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• 제19권1호
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• pp.25-36
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• 2012

Social Commerce recently attracted the attention of academic and industry researchers. Social Commerce aims to make a transactional environment which is beneficial to three parties-social commerce service provider, buyer and seller by way of using the platform of SNS. As Social Commerce is a new technology issue, there is no existing conceptual framework, e.g. appropriate classification the business types, that help to understand the nature of Social Commerce. This study suggests one classification framework and tries to verify whether it works.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• 한국소프트웨어감정평가학회 논문지
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• 제17권1호
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• pp.59-66
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• 2021

Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

• 한국안전학회지
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• 제34권5호
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• pp.119-131
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• 2019

Among the themes for CSR(Corporate Social Responsibility), consumer issues include protecting the health and safety of consumers who purchase and use the products. In particular, ensuring product safety is a major theme of consumer issues for corporate social responsibility. Currently, the government implements the Electrical Appliances and Consumer Products Safety Control Act for product safety management and selects products that may harmful to consumers as safety control items, and manages the products by designating them as 4 types of safety certification, safety confirmation, supplier conformity verification, and safety standard compliance. In this paper, we propose management plans for the establishment of a more reasonable classification type of safety management target for 48 items of consumer products to be controlled by the act, and confirm the validity of the plan. First, we perform cluster analysis using data for CISS (Consumer Injury Surveillance System) to derive a new classification type of the safety management target. Next, we compare the results of the cluster analysis with the classification type of the act and the existing scenario classification method RAS (Risk Assessment by Scenario) and the causal network method RAMP (Risk Assessment Method based on Probability). Based on these results, we propose two new plans of safety management target classification and verify its validity.

• 한국도서관정보학회지
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• 제48권3호
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• pp.63-81
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• 2017

이 연구는 사회복지학의 학문적 특성과 분류체계를 비교 분석함으로써, 사회복지학의 분류특성과 문제점을 분석하고 이를 토대로 KDC 제6판 사회복지학의 분류체계 수정 전개 방안을 제시하였으며, 연구결과를 요약하면 아래와 같다. 첫째, 사회복지학의 주요 영역에는 사회복지정책 및 행정을 비롯한 사회복지 일반, 각종 사회사업, 장애인, 아동청소년, 노인, 여성, 가족 등 각 계층이나 집단별 복지서비스가 포함된다. 둘째, 국립중앙도서관의 유별 자료 분석결과, 상대적으로 복지정책 및 행정, 연금, 보호 서비스, 다문화가족을 비롯한 소외계층 지원 등의 주제어에 빈도수가 높게 나타났으며, 이들 항목에 대한 세목을 신설하였다. 셋째, 분류항목의 수정 전개는 원칙적으로 사회복지학의 학문적 특성과 주제어 분석을 바탕으로 하였으며, 혼란을 줄일 수 있도록 가능한 한 기존의 KDC 분류체계를 유지하였다.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.163-170
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• 2021

빅데이터가 여러 분야에서 다양하게 접목됨에 따라 빅데이터 시장이 하드웨어로부터 시작해서 서비스 소프트웨어 부문으로 확장되고 있다. 특히 빅데이터 의미 파악 및 이해 능력, 분석 결과 등 총체적이고 직관적인 시각화를 위하여 애플리케이션을 제공하는 거대 플랫폼 시장으로 확대되고 있다. 그 중에서 SNS(Social Network Service) 등과 같은 소셜 미디어를 활용한 빅데이터 추출 및 분석에 대한 수요가 기업 뿐만 아니라 개인에 이르기까지 매우 활발히 진행되고 있다. 그러나 이처럼 사용자 트렌드 분석과 마케팅을 위한 소셜 미디어 데이터의 수집 및 분석에 대한 많은 수요에도 불구하고, 다양한 소셜 미디어 서비스 인터페이스의 이질성으로 인한 동적 연동의 어려움과 소프트웨어 플랫폼 구축 및 운영의 복잡성을 해결하기 위한 연구가 미흡한 상태이다. 따라서 본 논문에서는 소셜 미디어 데이터의 수집에서 추출 및 분류에 이르는 과정을 하나로 통합하여 운영할 수 있는 프레임워크를 개발하는 방법에 대해 제시한다. 제시된 프레임워크는 이질적인 소셜 미디어 데이터 수집 채널의 문제를 어댑터 패턴을 통해 해결하고, 의미 연관성 기반 추출 기법과 주제 연관성 기반 분류 기법을 통해 소셜 토픽 추출과 분류의 정확성을 높였다.

• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2003년도 춘계학술발표대회 논문집
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• pp.39-43
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• 2003

This study aimed to understand changing of work organization on variation of social organization and research typological classification of office layout based on preceded understanding. Buildings result from social needs and accommodate a variety of functions-economic, social, political, religious and cultural. Therefore, We can explain historical development of the constructing a building we understand the society and studying, After The modern age, it select a three buildings that there is an historical value of office Layouts planning and comprehend that make use sampling type of office work structure, studies a felicitous Typological classification of office Layouts. They find the development direction of a hereafter office of the task organization out according to it, And we suggest to Typological classification of Office Layouts based on Organization Theories.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5594-5615
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• 2019

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.