• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.120-127
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• 2020

The use of digital signage in the public and commercial communication areas has been increasing in recent years. By integrating cutting-edge information technologies such as 5G, artificial intelligence, and the Internet of Things, digital signage continues to break apart from traditional outdoor advertising media. This study identified the problems facing the domestic digital signage industry by exploring and analyzing major issues related to digital signage and derived future development measures. Specifically, online documents were collected based on the digital signage-related keywords created over the past 12 years to conduct big data network analysis, and key topics were derived through visualization of the results. This study has great policy implications in that it excluded biased interpretations based on the viewpoints of companies or the government and, more objectively, suggested the direction of the digital signage industry's development in the domestic media market.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.933-937
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• 2009

Interactive multi-media advertising is a new field with broad space of research coming into being with the emergence and extensive application of computer multi-media technology. This text analyzes the existing application in the advertisement of mutual media at first. Secondly analyze the public service advertisement design in the mutual media and public space. Through to in the public space, utilize to public service advertisement design and analysis of the mutual media, To characteristic and advantage of the mutual media, make the application of the public service advertisement to carry on research on the public space.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.105-110
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• 2005

Painting and writing are exceedingly different in the way they are visually express. However, if one were to trace the roots of both painting and writing, one would discover the two have the same origins. At one time, both painting and writing fulfilled the function portraying mutual or reciprocal messages to the masses. On the other hand, after the time of medieval manuscript and typography, they were separated completely into image and text. Today, the images are interpreted into the text. Meanwhile, the text better communicates its ideas to the masses through additional support of the simultaneous image. The joint use of the shaping of mutual exchanges between literature and visual art is being enlivened between scholars through their tendency to converse or communicate through the media. Moreover, the visual configuration and characters that strike into the new media are one of the various uses and the significantly important methods of deliberative communication that are necessary for hypertext functioning. In the following text, I will expande and develope the historical communication scheme and the process of modification of painting and writing, lead the origination of modern new-pictograph.

• Smart Media Journal
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• v.12 no.11
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• pp.77-85
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• 2023

After COVID-19, communication through online platforms has increased, leading to an accumulation of massive amounts of conversational text data. With the growing importance of summarizing this text data to extract meaningful information, there has been active research on deep learning-based abstractive summarization. However, conversational data, compared to structured texts like news articles, often contains missing or transformed information, necessitating consideration from multiple perspectives due to its unique characteristics. In particular, vocabulary omissions and unrelated expressions in the conversation can hinder effective summarization. Therefore, in this study, we restructured by considering the characteristics of Korean conversational data, fine-tuning a pre-trained text summarization model based on KoBART, and improved conversation data summary perfomance through a refining operation to remove redundant elements from the summary. By restructuring the sentences based on the order of utterances and extracting a central speaker, we combined methods to restructure the conversation around them. As a result, there was about a 4 point improvement in the Rouge-1 score. This study has demonstrated the significance of our conversation restructuring approach, which considers the characteristics of dialogue, in enhancing Korean conversation summarization performance.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.178-184
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• 2023

Finding texts in general scene images and recognizing their contents is a very important task that can be used as a basis for robot vision, visual assistance, and so on. However, for the low-resolution text images, the degradations, such as noise or blur included in text images, are more noticeable, which leads to severe performance degradation of text recognition accuracy. In this paper, we propose a new Korean text image super-resolution based on a Transformer-based model, which generally shows higher performance than convolutional neural networks. In the experiments, we show that text recognition accuracy for Korean text images can be improved when our proposed text image super-resolution method is used. We also propose a new Korean text image dataset for training our model, which contains massive HR-LR Korean text image pairs.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.59-71
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• 2018

Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.118-141
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• 2019

In South Korea, as awareness of gender equality increased since the 1990s, policies for gender equality and social awareness of equality have been established. Until recently, however, the gap between men and women in social and economic activities has not reached the globally desired level and led to social conflict throughout the country. In this study, we analyze the content of online news comments to understand the public perception of gender equality and the details of gender conflict and to grasp the emergence and diffusion process of emerging issues on gender equality. We collected text data from the online news that included the word 'gender equality' posted from January 2012 to June 2017 and also collected comments on each selected news item. Through text mining and the temporal semantic network analysis, we tracked the changes in discourse on gender equality and conflict. Results revealed that gender conflicts are increasing in the online media, and the focus of conflict is shifting from 'position and role inequality' to 'opportunity inequality'.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1337-1341
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• 2015

Background: Breast cancer is the most common cancer and the second principal cause of cancer deaths among women worldwide, including Malaysia. This study focused on media choice and attempted to determine the communication channels mostly used and preferred by women in seeking information and knowledge about breast cancer. Materials and Methods: A cross sectional study was carried out to examine the breast cancer prevention information seeking behavior among 450 students at one private university in Malaysia. Results: The mean age of respondents was $25{\pm}4.3years$. Common interpersonal information sources were doctors, friends, and nurses and common channel information sources were television, brochure, and internet. Overall, 89.9% used cell phones, 46.1% had an interest in receiving cell phone breast cancer prevention messages, 73.9% used text messaging, and 36.7% had an interest in receiving text breast cancer prevention messages. Bivariate analysis revealed significant differences among age, eduation, nationality and use of cell phones. Conclusions: Assessment of health information seeking behavior is important for community health educators to target populations for program development.

• Smart Media Journal
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• v.9 no.3
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• pp.98-106
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• 2020

Currently, as Internet users increase, the use of swearword is indiscriminately increasing. As a result, cyber violence among teenagers is increasing very seriously, and among them, cyber-language violence is the most serious. In order to eradicate cyber-language violence, research on detection of swearword has been conducted, but the method of detecting swearword by looking at the meaning of words and the flow of context is insufficient. Therefore,in this paper,we propose a method of detecting swearword using FastText model and LSTM model so that deliberately modified swearword and standard language can be accurately detected by looking at the flow of context.