독후감 텍스트의 토픽모델링 적용에 관한 탐색적 연구

이수상 한국도서관·정보학회 2016 한국도서관정보학회지 Vol.47 No.4

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the “topicmodels” package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports’ topics combining with network analysis. 이 연구는 독후감 텍스트의 주제분석에 토픽모델링의 활용방안을 탐색하는 것을 목적으로 하고 있다. 텍스트의 주제분석 방안으로서 토픽모델링 분석방법을 이해하고, R에서 제공하는 “topicmodels” 패키지의 LDA 함수를 사용하여 23건의 사례 독후감 텍스트들을 대상으로 실제의 분석작업을 수행하였다. 토픽모델링 분석결과 16개의 토픽들을 추출하였고, 토픽과 구성 단어들의 관계에서 토픽 네트워크, 사례 독후감과 토픽들의 관계에서 독후감 네트워크를 구성하였다. 이후 토픽 네트워크와 독후감 네트워크를 대상으로 중심성 분석을 수행하였으며, 분석결과는 다음과 같다. 첫째, 16개의 토픽들이 1개의 컴포넌트를 가지는 네트워크로 나타났다. 이것은 16개 토픽들이 상호 연관되어 있다는 것을 의미한다. 둘째, 독후감 네트워크에서는 연결정도 중심성이 높은 독후감들과 낮은 독후감들로 구분이 되었다. 전자의 독후감들은 다른 독후감들과 주제적으로 유사성을 가지며, 후자의 독후감들은 다른 독후감들과 주제적으로 상이성을 가지는 것으로 해석하였다. 토픽모델링의 결과를 네트워크 분석과 결합함으로써 독후감의 주제파악에 유용한 결과들을 얻게 되었다.

Prediction Model of Sports Performance Based on Grey BP Neural Network

Deng Kui,Xiao Liu,Xu Liang,Song Haiyan 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.8

The best annual performances of the world women’s pentathlons during 2005~2013 are statistically collected in this article, and the prediction of the best performance of the world women’s heptathlon in 2013 is taken as the research object. According to the best annual performances of the world women’s heptathlons during 2005~2012, the sports performance prediction model composed of GM(1,1) grey prediction model and BP neural network prediction model in serial connection is established in this article, and this model is applied to predict the best annual performance of the world women’s heptathlon in 2013. Through the comparison of the actual value of the best annual performance of the world women’s heptathlon in 2013 and the predicted value of the model, the application of the grey BP neural network prediction model in sports performance prediction is researched and analyzed in this article. The research result shows that for the sports performance prediction problem, the grey BP neural network prediction model has the features of high prediction accuracy, simple application and strong generalization performance, and this model is also superior to single GM(1,1) grey prediction model and BP neural network model.

동적 소셜네트워크 구조 변수를 적용한 가상 재화 구매 모형 연구

이희태,배정호 한국유통과학회 2019 유통과학연구 Vol.17 No.3

Purpose – The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node’s network position can fluctuate considerably over time and the node’s network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time–invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology – The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results – First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion – The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

사회선택 통계모형의 방법론적 특성과 p2와 p*모형을 활용한 남자고등학생의 영어도움 네트워크 분석

김종민(Kim, Chong Min) 한국교육평가학회 2015 교육평가연구 Vol.28 No.3

본 연구의 목적은 사회 네트워크 분석 중 사회선택 통계모형의 방법론적 특성을 탐색하고 이를 활용하여 남자고등학생의 영어도움 네트워크 구조를 분석하는 것이다. 이에 첫째, 사회선택 통계모형인 p2 와 p* 모형을 중심으로 방법론적 특성을 탐색하였고, 둘째, 경기도 소재 남자 고등학생의 영어도움네트워크 자료를 토대로, p2 와 p* 모형을 활용하여 네트워크의 구조(호혜성과 전이성)와 고등학생의 특성(성취목표, 영어 학습동기 및 영어 학업성취도)이 영어도움 네트워크에 어떤 영향을 주는지 분석하였다. 먼저 방법론적 특성으로 p* 모형은 p2 모형에서 모형화하기 힘들었던 전이성을 포함한 복잡한 네트워크 구조를 모형 명세화 할 수 있는 반면, p2 모형은 전이성 등의 복잡한 네트워크 구조가 존재하지않는 네트워크나 네트워크 자체가 별로 없는 경우 네트워크의 다변량 분석과 다층분석 그리고 다양한모형 명세화가 가능하고 또한 추정의 수렴성과 추정의 표준오차의 효율성 측면에서 유리하다. 그리고 남자고등학생의 영어도움 네트워크를 p2 와 p* 모형으로 분석결과, 남자고등학생의 특성인 사회적 수행접근목표, 영어 학습 내재적 동기 그리고 영어 학업성취도 점수가 높을수록 영어도움 네트워크가 증가 하였고, 네트워크 구조인 호혜성과 전이성은 같은 반 남자 고등학생의 영어도움 네트워크에 긍정적인영향을 주었다. The purpose of this study is to explore methodological characteristics of social network analysis with a focus on social selection models and to analyze male high schools students’ help networks for English learning using p2 and p* models. Among social selection models, p2 and p* models were presented as statistical models and methodological characteristics of both models were introduced. Each social selection statistical mdoel has its own methodological characteristics. p* models can specify complex network structure like transitivity which could hardly be specified in p2 models. On the other hand, p2 models can analyze multivariate models and multilevel models and specify various models when social networks are sparse or simple without complex network structure-like transitivity, with an advantage in the convergence of estimation and efficiency of estimation errors. The application of p2 and p* models was demonstrated using male high school students’ social networks for English learning in Korea. The research question was to examine which attributes(achievement goals, English-learning motivation, and English achievement scores) and network structure(reciprocity and transitivity) affect English-learning help networks. The results of p2 and p* models indicated that the transitive triplets(transitivity), social performance-approach goals, the intrinsic motivation to learn English, and English achievement scores were statistically significant and positively related to providing and seeking help for English learning among students in the same classroom.

Performance Prediction Model of University Students Based on the Grey BP Neural Network

Liao Yu,Liu Zongxin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.10

This article counted the best performance of students entrepreneurship courses from 2005 to 2014, and took the best performance prediction of 2014 entrepreneurship course as the research object. According to the best annual performance of entrepreneurship courses from 2005 to 2014, this article established the grade prediction model of series combination of GM (1, 1) grey prediction model and BP neural network prediction model, and the established model was used to predict the best annual performance of students entrepreneurship course. Through comparing the actual value of the best annual performance of 2014 entrepreneurship course and the predicted value c by the model, this article analyzed the application of grey BP neural network prediction model in the students entrepreneurship performance prediction. The research results showed that for entrepreneurship performance prediction problem, the grey BP neural network prediction model had high prediction precision , simple application, and it can be widely used, and had more advantages than single GM (1, 1) grey prediction model and BP neural network model.

Application of Neural Network Model to Vehicle Emissions

김대현,이정 서울시립대학교 도시과학연구원 2010 도시과학국제저널 Vol.14 No.3

The issue of air quality is now a major concern around the world and the vehicle emissions model is very important. Most of the current vehicle emission models are multiple regression techniques. In this study, a neural network-based model has been proposed to achieve better estimation accuracy. The estimation performance of two models, the proposed neural network-based model and a general regression model, has been compared using mean absolute error (MAE). A comparative study between two models to estimate vehicle emissions, the proposed neural network-based model and a general regression model, has been conducted to assess the estimation performance of the proposed model in terms of mean absolute percentage error. Experimental results in this study revealed that the neural network model performed better as it was able to decrease the error for emission estimation comparing with the multiple regression models. More importantly, in this study a lookup table (LUT) method has been proposed to overcome the black-box problem, which is a disadvantage of the neural network models. It could be useful for any other researches to estimate emissions without developing and training the neural network model which can be a time-consuming task. The issue of air quality is now a major concern around the world and the vehicle emissions model is very important. Most of the current vehicle emission models are multiple regression techniques. In this study, a neural network-based model has been proposed to achieve better estimation accuracy. The estimation performance of two models, the proposed neural network-based model and a general regression model, has been compared using mean absolute error (MAE). A comparative study between two models to estimate vehicle emissions, the proposed neural network-based model and a general regression model, has been conducted to assess the estimation performance of the proposed model in terms of mean absolute percentage error. Experimental results in this study revealed that the neural network model performed better as it was able to decrease the error for emission estimation comparing with the multiple regression models. More importantly, in this study a lookup table (LUT) method has been proposed to overcome the black-box problem, which is a disadvantage of the neural network models. It could be useful for any other researches to estimate emissions without developing and training the neural network model which can be a time-consuming task.

Lightweight object detection network model suitable for indoor mobile robots

Lin Jiang,Wenkang Nie,Jianyang Zhu,Xumin Gao,Bin Lei 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.2

This work proposes a lightweight object detection network model ShuffleNetSSD (S-SSD) to solve the problem of single shot multibox detector (SSD) network model where it cannot meet the real-time performance requirement in the task of object detection and recognition of indoor mobile robot. This model is suitable for indoor mobile robot by improving the SSD network model based on ShuffleNet network. The main idea of the improvement is that SSSD replaces VGG-16 network as the basic feature extraction network of SSD network model with ShuffleNet network. The proposed model is based on the design of deep separable convolution, point-by-point grouping convolution, and channel rearrangement. It retains the design idea of multiscale feature graph detection of SSD network model. This model ensures a slight decline in detection accuracy while greatly reduces the amount of computation generated by the network operation, thereby greatly improving the detection rate. A data set for the task of object detection and recognition of indoor mobile robot is made. The S-SSD lightweight network model is superior to the original SSD network model and tiny-YOLO lightweight network model in terms of detection accuracy and detection rate, and can simultaneously meet the requirement of detection accuracy and real-time performance in the task of indoor object detection and recognition of mobile robot. These findings are verified through the comparative experiments of object detection accuracy and detection rate and real-time object detection and recognition of mobile robot under the actual indoor scene.

소셜네트워크분석을 통한 Supportive Design 트렌드 연구

이유정 한국정보시스템학회 2025 정보시스템연구 Vol.34 No.1

Purpose This study aims to systematically analyze research trends in Supportive Design using text mining techniques and Social Network Analysis (SNA). The goal is to identify the main topics and core keywords within Supportive Design research and to visualize the relationships between studies to better understand the latest research trends. Design/methodology/approach For this study, 214 Supportive Design-related papers were initially collected from major academic databases, including ScienceDirect, PubMed, and EBSCO. Using LDA topic modeling, six main research topics were identified. Based on the extracted keywords, an additional 3,497 papers were collected, and text mining techniques were applied to extract titles and keywords. Subsequently, LDA topic modeling and Social Network Analysis (SNA) were conducted to analyze the core keywords for each topic, examine the relationships between studies, and visualize the resulting network. Findings The analysis revealed that Supportive Design research can be classified into six main topics: Elderly and Universal Healthcare Environments, Healing Environments and Evidence-Based Hospital Design, Hospital Space Planning and Public Healthcare, Patient-Centered Healthcare Environments and Decision-Making, Mental Health and Healing Models, and Medical Technology and Well-being. Key keywords were extracted for each topic, and the Social Network Analysis (SNA) identified strong interconnections among studies, with keywords such as “healing”, “care”, “design”, and “health” showing high centrality. Purpose This study aims to systematically analyze research trends in Supportive Design using text mining techniques and Social Network Analysis (SNA). The goal is to identify the main topics and core keywords within Supportive Design research and to visualize the relationships between studies to better understand the latest research trends. Design/methodology/approach For this study, 214 Supportive Design-related papers were initially collected from major academic databases, including ScienceDirect, PubMed, and EBSCO. Using LDA topic modeling, six main research topics were identified. Based on the extracted keywords, an additional 3,497 papers were collected, and text mining techniques were applied to extract titles and keywords. Subsequently, LDA topic modeling and Social Network Analysis (SNA) were conducted to analyze the core keywords for each topic, examine the relationships between studies, and visualize the resulting network. Findings The analysis revealed that Supportive Design research can be classified into six main topics: Elderly and Universal Healthcare Environments, Healing Environments and Evidence-Based Hospital Design, Hospital Space Planning and Public Healthcare, Patient-Centered Healthcare Environments and Decision-Making, Mental Health and Healing Models, and Medical Technology and Well-being. Key keywords were extracted for each topic, and the Social Network Analysis (SNA) identified strong interconnections among studies, with keywords such as “healing”, “care”, “design”, and “health” showing high centrality.

공론화결정의 정책네트워크특성 비교연구: 신고리 5・6호기사례와 제주 녹지국제병원사례

배봉준,김주환 서울대학교 한국행정연구소 2022 행정논총 Vol.60 No.1

The purpose of this study is to compare and explain the characteristics of policy networks appearing from policy decisions on public issues under debate in two different policy areas based on the policy network model. The results of the empirical analysis found that the characteristics of the policy networks making policy were different in the two policy cases. A modified policy community appeared in the case of Shin-Gori No. 5&6, while a modified issue network appeared in the case of JeJu International Green Hospital. In the former case, a number of actors officially proposed consensus policy recommendations based on a wide range of interests through frequent and high-level interactions. On the other hand, in the latter case, a number of actors had conflicting policy recommendations based on narrow interests through formal but intermittent and low-level interactions. Therefore, it can be said that the difference in the type of policy network occurring in the two policy cases was caused by the difference in network structure-actors, integration, power, and so forth. The implications derived from the results of this study are that public debate decisions were analyzed by applying a policy network model for the first time. The results of this study theoretically prove Rhodes & Marsh's policy network model, but there is a policy-making phenomenon transformed from the basic form. Real policy decisions are not strictly divided into policy communities or issue networks, but are made in a modified policy network in which the two types are mixed. The meaning of this modified type of policy decision implies the need for multi-layered network policy decisions in the era of the governance paradigm in Korea unlike government-led policy decisions in the era of the government paradigm. However, it can be seen that policy decisions are influenced by the environmental context and the characteristics of the political system in each country, as well as the influence and activities of interest groups and civic groups acting in society. 이 연구의 목적은 정책네트워크모형을 토대로 두 가지 상이한 정책영역에서 발생한 공론화의제의 정책결정에서 나타나는 정책네트워크특성을 비교 설명하는 것이다. 경험적인 분석결과는 두 가지 정책사례의 정책결정에서의 정책네트워크특성이 상이함을 발견하였다. 신고리 5・6호기 공론화사례에서는 ‘변형된 정책공동체’가, 제주 녹지국제병원사례에서도 ‘변형된 이슈네트워크’가 나타난 것이다. 전자 사례에서는 다수의 행위자가 넓은 이익을 토대로 공식적으로 빈번하고 높은 수준의 상호작용을 통하여 합의적인 정책권고안을 제시하였다. 반면에 후자 사례에서는 다수의 행위자가 좁은 이익을 토대로 공식적이지만 간헐적이고 낮은 수준의 상호작용을 통하여 갈등적인 정책권고안을 산출하였다. 따라서 두 가지 정책사례에서 정책네트워크유형의 차이가 발생한 것은 네트워크- 행위자, 통합, 권력-의 차이가 원인이라고 할 수 있다. 본 연구결과로부터 도출되는 함의는 처음으로 공론화결정을 정책네트워크모형을 적용하여 분석하였다는 점이다. 본 연구결과는 이론적으로 Rhodes & Marsh의 정책네트워크모형을 입증하고 있지만 기본형으로부터 변형되는 정책결정현상이 나타나고 있다. 현실의 정책결정은 엄격하게 정책공동체나 이슈네트워크로 분리되어 이루어지는 것이 아니라 두 가지 유형이 혼합되는 변형된 정책네트워크 속에서 이루어지고 있다. 이러한 변형된 유형의 정책결정이 가지는 의미는 우리나라의 정책결정도 정부패러다임시대의 정부주도적인 정책결정으로부터 새로운 거버넌스패러다임시대의 다층수준 네트워크적 정책결정의 필요성을 암시한다. 그러나 정책결정은 각국이 직면하는 환경맥락과 정치체제의 특성 및 사회에서 분출하는 이익집단이나 시민단체의 영향력과 활동의 영향을 받는다고 볼 수 있다.

네트워크 중심성 척도가 추천 성능에 미치는 영향에 대한 연구

이동원(Dongwon Lee) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.1

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer’s network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer’s purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months’ records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implie