• International Journal of Contents
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• v.15 no.1
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• pp.10-24
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• 2019

This study applied network agenda-setting theory to analyze the impact of the agenda-setting function of the media on certain issues by focusing on the agenda at the center of controversy, 'Creative Economy'. To this end, the study extracted the data referred to creative economy in the media and SNS from 1 January 2008 to 31 December 2014, and analyzed the data using the network analysis program UCINET and the Korean language analysis program Textom. The results of the present study show that, during the period under former President Lee (2008-2011), the media's creative economy agenda-setting function did not exert a significant impact on the agenda-setting within SNS. However, from 2012 when the government of former President Park Geun-hye had started, the agenda-setting function of the media starts to show increasingly strong influence on the agenda cognition in SNS. The central words and sub-words configuration forming the center of the semantic network moved in the direction of a high correlation, in addition to the gradually increasing correlation based on QAP correlation analysis. In 2014, the semantic networks of the media and SNS bore a close resemblance to each other, while the shape of networks and sub-words structure also had a high level of similarity.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.99-108
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• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• Asia pacific journal of information systems
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• v.29 no.3
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• pp.489-502
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• 2019

The WeChat mini program is an application embedded in WeChat that users can use without downloading and installing. After it was officially released in 2017, many travel enterprises have launched their own mini programs on the WeChat platform. This study applies affordance theory to investigate the WeChat mini program's role in tourism activities through social network analysis using the R programming language. The authors searched the topic of "how do you perceive the travel-related WeChat mini program" and then crawled the 200 comments found; 180 comments were analyzed after data cleansing. The results show that travel-related WeChat mini programs play a very important role in Chinese social network tourism activities. This paper found that WeChat played a more active role in various tourism-related interactions with Chinese social networks. Moreover, the results show how affordance theory is applied to the use of WeChat mini programs.

• Journal of the Korean Society of Earth Science Education
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• v.9 no.1
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• pp.65-87
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• 2016

The purpose of this study is to examine Korean journals related to science education in order to analyze research trends into Nature of science in Korea. The subject of the study is the level of Korean Citation Index (KCI-listed, KCI listing candidates), that can be searched by the key phrase, "Nature of science" in Korean language through the RISS service. In this study, the Descriptive Statistical Analysis Method is utilized to discover the number of research articles, classifying them by year and by journal. Also, the Sementic Network Analysis was conducted to Word Cloud Analysis the frequency of key words, Centrality Analysis, co-occurrence and Cluster Dendrogram Analysis throughout a variety of research articles. The results show that 91 research papers were published in 25 journals from 1991 to 2015. Specifically, the 2 major journals published more than 50% of the total papers. In relation to research fields., In addition, key phrases, such as 'Analysis', 'recognition', 'lessons', 'science textbook', 'History of Science' and 'influence' are the most frequently used among the research studies. Finally, there are small language networks that appear concurrently as below: [Nature of science - high school student - recognize], [Explicit - lesson - effect], [elementary school - science textbook - analysis]. Research topic have been gradually diversified. However, many studies still put their focus on analysis and research aspects, and there have been little research on the Teaching and learning methods.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.425-434
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• 2001

In this paper we introduce a general framework for the modeling, analysis and costing of logistic networks including supply chains (SCs). The employed modeling notation, the so-called Process Chain paradigm, is specifically developed for the application field of logistic networks which includes SCs. We view SCs as discrete event dynamic systems (DEDS) and apply corresponding simulative techniques in order to derive performance measures of the Process Chain model under investigation. For this purpose Process Chain models are automatically transformed into the input language of the simulation tool HIT. Subsequently, a cost accounting model using the performance measures is applied to obtain costs which are actually subject of interest. The usefulness and applicability of the approach is illustrated by a typical supply chain example. We investigate the impact of an additional SC channel between a manufacturer and web-consumers on the overall supply chain costs.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.15-28
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• 1997

Longterm memory is encoded in the neuronal connectivities of the brain. The most successful models of human memory in their operations are models of distributed and self-organized associative memory, which are founded in the principle of simulaneous convergence in network formation. Memory is not perceived as the qualities inherent in physical objects or events, but as a set of relations previously established in a neural net by simultaneousy occuring experiences. When it is easy to find correlations with existing neural networks through analysis of network structures, memory is automatically encoded in cerebral cortex. However, in the emergence of informations which are complicated to classify and correlated with existing networks, and conflictual with other networks, those informations are sent to the subcortex including hippocampus. Memory is stored in the form of templates distributed across several different cortical regions. The hippocampus provides detailed maps for the conjoint binding and calling up of widely distributed informations. Knowledge about the distribution of correlated networks can transform the existing networks into new one. Then, hippocampus consolidats new formed network. Amygdala may enable the emotions to influence the information processing and memory as well as providing the visceral informations to them. Cortico-striatal-pallido-thalamo-cortical loop also play an important role in memory function with analysis of language and concept. In case of difficulty in processing in spite of parallel process of informations, frontal lobe organizes theses complicated informations of network analysis through temporal processing. With understanding of brain mechanism of memory and information processing, the brain mechanism of mental phenomena including psychopathology can be better explained in terms of neurobiology and meuropsychology.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.243-246
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• 2018

최근 사용자 발화를 이해하고 그에 맞는 피드백을 생성할 수 있는 대화 시스템의 중요성이 증가하고 있다. 따라서 사용자 의도를 파악하기 위한 화행 분석은 대화 시스템의 필수적인 요소이다. 최근 많이 연구되는 심층 학습 기법은 모델이 데이터로부터 자질들을 스스로 추출한다는 장점이 있다. 발화 자체의 연속성과 화자간 상호 작용을 포착하기 위하여 CNN에 RNN을 결합한 CNN-RNN을 제안한다. 본 논문에서 제안한 계층 구조 어텐션 매커니즘 기반 CNN-RNN을 효과적으로 적용한 결과 워드 임베딩을 추가한 조건에서 가장 높은 성능인 91.72% 정확도를 얻었다.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.358-380
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• 2016

In order to considerably reduce the ambiguity rate, we propose in this article a disambiguation approach that is based on the selection of the right diacritics at different analysis levels. This hybrid approach combines a linguistic approach with a multi-criteria decision one and could be considered as an alternative choice to solve the morpho-lexical ambiguity problem regardless of the diacritics rate of the processed text. As to its evaluation, we tried the disambiguation on the online Alkhalil morphological analyzer (the proposed approach can be used on any morphological analyzer of the Arabic language) and obtained encouraging results with an F-measure of more than 80%.