• Journal of Korea Entertainment Industry Association
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• v.15 no.7
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• pp.137-143
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• 2021

This discussion can be said to be a qualitative study on the possibility of linking communication education for college students and literacy education for Korean language-linked educators based on the theory of interpretation of cognitive meaning of media text containing complex mechanisms. The implicit meaning of media content expression used as an interactive communication strategy will be accepted as a multilateral interpretation according to the individual learner's cognitive environment. If so, how is the general media content meaning intended by the content creator being accepted? These doubts are the starting point for discussion. To solve the problem, I leaned on the experimental pragmatic methodology of cognitive aesthetics and applied a model of relevance of cognitive linguistics to connect learners' creative cognitive environment and present content to find a contrast. As a result of the discussion, it was possible to establish a basic framework for learners to express their subjectivity and creative thinking that could connect the cognitive environment and present content themselves. In particular, active and positive learners also revealed direct descriptive expressions to build a new cognitive environment, such as suggesting a third alternative to argue the ability to question produced media texts and the validity of the meaning implied in the text. In the future, since media text containing complex mechanisms is an indirect and persuasive communication behavior that occurs easily through various media in modern society, the universal communication principle of reliable conversation between media text creators and audiences should exist.

• Cross-Cultural Studies
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• v.27
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• pp.347-386
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• 2012

This article aims to establish the model of textual communication and its schema. To do this, we must identify the characteristics of textual communication, different from that of the oral, because the model of communication is usually done to show the structure of oral communication. Moreover, we must clarify the status text as '${\acute{e}}nonc{\acute{e}}$', that is to say product of the act of enunciation. The study of the text has now reached to achieve from the perspective of pragmatics, overcoming the structural point of view that dominates long text linguistics. And now, we need to enrich the theoretical basis of the pragmatics of text. Then the search of elements necessary to develop the model and pattern of textual communication can help to establish the elements used to form the theoretical basis. To clarify the characteristics of textual communication, we needed to explain the present communication by the position of reader and the point of view of textual reference. The schema that we proposed is not perfect, but there are still issues to think to complete it. For example, one must take into account the plurality of readers and reflect the relationship between interpretive texts in this schema, etc. This kind of problem is not only required to complete the schema but also to strengthen the basis of the theory of textual communication and the pragmatics of text.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.621-623
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• 2022

최근 코로나19로 인해 비대면으로 소통할 수 있는 플랫폼에 대한 관심이 증가하고 있으며, 가상 세계의 개념을 도입한 메타버스 플랫폼이 MZ세대의 새로운 SNS로 떠오르고 있다. 아바타를 통해 상호 교류가 가능한 메타버스는 텍스트 기반의 소통뿐만 아니라 음성과 동작 시선 등을 활용하여 변화된 의사소통 방식을 사용한다. 음성을 활용한 소통이 증가함에 따라 다른 이용자에게 불쾌감을 주는 혐오 발언에 대한 신고가 증가하고 있다. 그러나 기존 혐오 발언 탐지 시스템은 텍스트를 기반으로 하여 사전에 정의된 혐오 키워드만 특수문자로 대체하는 방식을 사용하기 때문에 음성 혐오 발언에 대해서는 탐지하지 못한다. 이에 본 논문에서는 인공지능을 활용한 음성 혐오 표현 탐지 시스템을 제안한다. 제안하는 시스템은 음성 데이터의 파형을 통해 은유적 혐오 표현과 혐오 발언에 대한 감정적 특징을 추출하고 음성 데이터를 텍스트 데이터로 변환하여 혐오 문장을 탐지한 결과와 결합한다. 향후, 제안하는 시스템의 현실적인 검증을 위해 시스템 구축을 통한 성능평가가 필요하다.

• Journal of Korean Library and Information Science Society
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• v.32 no.2
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• pp.349-363
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• 2001

The study suggests information processing model and discusses variety of information due to change and expansion of text, comparies information service in cyberspace and reality and presents reference interview in on - line service.

• Archives of design research
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• v.17 no.1
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• pp.201-210
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• 2004

The web as a sign synthesis text has become a kernel for incorporeal knowledge as well as a communication model through the ubiquitous environment all over the world. The evaluation of the communication model, which is essential for the information structure, acts as an important basis on determining the quality of the web contents. In this study, the development of the progress of the communication of semantic meaning in the construction of the information structure was analyzed in views of the form, the function, and the emotional effect of the rich media contents of the web. The transformation process from the initial access elements through the final selection elements was suggested as the communication model and the effects of the function of the information in the web on the process was assessed by the engineering and linguistic models of Shannon, Weaver, and Roman Jakobson. The results of this study showed that the environments such as the speed, the memory space, data compression technique, and data filtering have influences on the web contents expression and the evaluation of the communication model in connection with the environments is the basis in the information structure.

• Smart Media Journal
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• v.5 no.3
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• pp.54-61
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• 2016

According to many smart devices are development, SNS(Social Network Service) users are getting higher that is possible for real-time communicating, information sharing without limitations in distance and space. Nowadays, SNS users that based on communication and relationships, are getting uses SNS for information sharing. In this paper, we used the SNS posts for users to extract the category and information provider, how to following of recommend method. Particularly, this paper focuses on classifying the words in the text of the posts and measures the frequency using Inception-v3 model, which is one of the machine learning technique -CNN(Convolutional Neural Network) we classified image word. By classifying the category of a word in a text and image, that based on DMOZ to build the information provider DB. Comparing user categories classified in categories and posts from information provider DB. If the category is matched by measuring the degree of similarity to the information providers is classified in the category, we suggest that how to recommend method of the most similar information providers account.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.30-43
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• 2022

In this study, elementary students' science communication competence was investigated based on the grammatical features expressed in their language-use in classroom discourse and science writings. The classes were designed to integrate the evidence-based reasoning framework and traditional learning cycle and were conducted on fifth graders in an elementary school. Eight elementary students' discourse data and writings were analyzed using lexico-grammatical resource analysis, which examined the discourse text's content and logical relations. The results revealed that the student language used in analyzing data, interpreting evidence, or constructing explanations did not precisely conform to the grammatical features in science language use. However, they provided examples of grammatical metaphors by nominalizing observed events in the classroom discourses and those of causal relations in their writings. Thus, elementary students can use science language grammatically from science language-use experiences through listening to a teacher's instructional discourses or recognizing the grammatical structures of science texts in workbooks. The opportunities in which elementary students experience the language-use model in science learning need to be offered to understand the appropriate language use in the epistemic context of evidence-based reasoning and learn literacy skills in science.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.