• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.149-163
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• 2021

The 4th industrial revolution refers to the next-generation industrial revolution led by information and communication technologies such as artificial intelligence (AI), Internet of Things (IoT), robot technology, drones, autonomous driving and virtual reality (VR) and it also has made a significant impact on the development of the advertising industry. However, the world is rapidly changing to a non-contact, non-face-to-face living environment to prevent the spread of COVID 19. Accordingly, the role of the 4th industrial revolution and advertising is changing. Therefore, in this study, text analysis was performed using Big Kinds to examine the 4th industrial revolution and changes in advertising before and after COVID 19. Comparisons were made between 2019 before COVID 19 and 2020 after COVID 19. Main topics and documents were classified through LDA topic model analysis and Word2vec, a deep learning technique. As the result of the study showed that before COVID 19, policies, contents, AI, etc. appeared, but after COVID 19, the field gradually expanded to finance, advertising, and delivery services utilizing data. Further, education appeared as an important issue. In addition, if the use of advertising related to the 4th industrial revolution technology was mainstream before COVID 19, keywords such as participation, cooperation, and daily necessities, were more actively used for education on advanced technology, while talent cultivation appeared prominently. Thus, these research results are meaningful in suggesting a multifaceted strategy that can be applied theoretically and practically, while suggesting the future direction of advertising in the 4th industrial revolution after COVID 19.

• The Journal of Information Systems
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• v.30 no.3
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.243-254
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• 2022

Customers' perceptions of information about a company's products or services have altered as a result of the development of ICT and social networks. This gives rise to a fact that buzz marketing, which is a marketing technique employed commonly in today's business and communication, has a significant impact on customers' electronic word of mouth intention (e-WOM). However, very few studies about this issue have been conducted so far, which reveal a gap in understanding buzz marketing from an academic perspective. Based on the results of a cross-sectional survey in Binh Duong city, this study investigates the efficiency and effect of buzz marketing on customers' e-WOM intention through mediating variables of message credibility. Data from 367 time-lagged individual samples were collected and analyzed by the structural equation modeling method (SEM). Results showed that creativity, clarity, and humor variables have a positive relationship with message credibility and then impact the intention to conduct e-WOM of social networks' users. Marketing campaigns employing the buzz technique should be launched with easy-to-understand and entertainable messages. Findings from this study also provide managers with a scientific understanding of buzz marketing and the effectiveness of this technique as well as reveal the potential for future studies to explore further in this area.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.371-382
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• 2017

This study aims to analyze the research trend of cultural policy-related papers based on 832 key words among 186 whole articles in the Journal of Cultural Policy by the Korea Culture & Tourism Institute from October 2008 to January 2017. The analysis was performed using a big data analysis technique called the Semantic Network Analysis. The Semantic Network Analysis consists of frequency analysis, density analysis, centrality analysis including degree centrality, betweenness centrality, and eigenvector centrality. Lastly, the study shows a figure visualizing the results of the centrality analysis through Netdraw program. The most frequently exposed key words were 'culture', 'cultural policy/administration', 'cultural industry/cultural content', 'policy', 'creative industry', in the order. The key word 'culture' was ranked as the first in all the analysis of degree centrality, betweenness centrality and eigenvector centrality, followed by 'policy' and 'cultural policy/administraion'. The key word 'cultural industry/cultural content' with very high frequency recorded high points in degree centrality and eigenvector centrality, but showed relatively low points in betweenness centrality.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.669-678
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• 2015

This study aims at investigating students' perceptions and peer feedback to practicum for teaching English in the English Department at a Korean university. The participants were forty-two students at an elective course, 'Method for teaching English', and the data comprised questionnaire, 12 teams of practicum, and 15 sets of PF. A 'Word Count System (WCS)' was adopted to analyze the data. The findings show that students regarded 'practicum' (52.4%) as more important than 'teacher's lectures' (42.8%), and most students (80%) applied more than 70% of lesson plans to their practicums. The practicum gave them experience of a teacher, development of confidence, recognition on their weaknesses and values of teaching. While the strengths shown in PF were mainly 'teaching methods and technique', 'use of multimedia', and 'teaching materials', the weaknesses were 'classroom interactions', 'teaching methods and techniques' and 'use of blackboard'. Overall praises were 1.8 times more than the matters which needed to be developed. The conclusion suggest that the students had their own insights toward teaching practices and how learners can be motivated.

• The Research Journal of the Costume Culture
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• v.8 no.3
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• pp.411-421
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• 2000

Black froak coat, white shirts, top hat and cane has been the symbol of gentlemen in 19th and early of 20th century. The pattern invented by Savile Row in London prevailed whole England. Such a pattern has been the standard form for two hundred years all over the word, although it was replaced with a functionalism which developed in Italy and America at the end of 20th century. The clothes of gentlemen was developed by several factors ; English people respect a tradition. The clothes was practical, since the weather in England was bad. The success of Industrial Revolution made England wealthy. Various special clothes in sports, for example, riding, criket, golf and tennis also contribute the modern gentlemen clothes with advance tailoring technique. The change of gentlemenship with social environment, from Regency dandy, Romantic gentlemen to Muscular Christianity, was studied. Idial gentlemenship and development its clothes which is now the standard of modern men's wear also studied.

• Journal of Families and Better Life
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• v.22 no.6 s.72
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• pp.143-152
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• 2004

The purpose of this study was to know the effectiveness of prolonged-speech treatment program on school-age children with stuttering. Two male and One female subjects participated in this study. The speech of 3 subjects in the treatment was assessed on frequency of stuttering, stuttering Pattern, degree of severity in stuttering. This Program was taken from Ryan's the step of traditional therapy Program and prolonged-speech technique program. and then, modified in accordance with the purpose of this study. The treatment program were consisted of Four stages. The results of this study were as follows: First, 3 subjects can speak with greatly reduced stuttering frequency after treatment Second, in the stuttering pattern, all subjects were changed from part-word repetition in stuttering into a prolongation in stuttering. And also, all subjects showed similar effect in the maintenance.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.253-263
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• 2016

We analyzed the social issue 'image of the elderly' using Big Data and Social Network Analysis. First, we analyzed the words extracted by the text mining technique by inputting the keyword 'elderly'. As a result of analysis, the image of the elderly viewed through media such as cafes, blogs, etc. Representing the trend of the public was using the word 'Senior' the most. The image of the elderly is expressed using the word having the highest frequency in the top 10, "The elderly are 'Senior' people who are respected by society, they are organized to earn money, to earn their qualifications, to health, and to 'Seniors' who desire to work healthy up to 100 years old". The purpose of this study is to differentiate from the existing analysis method by analyzing the macro-level image of the elderly including the social discourse by collecting vast amount of data and analyzing it with the social networking technique. When the image of the elderly that the public perceives is positively expressed as 'Senior', it can be said that the direction of the current elderly policy is evaluated as a desirable direction. On the other hand, it was able to feel the 'desire' of the public who wanted to be evaluated. Therefore, the policy direction of the elderly to be applied in the future should be the policy that enables the elderly to be perceived as 'Necessary existence' in society by taking on social roles. In addition, we proposed to implement the policy of the elderly that reflects priorities such as job creation, welfare, and alienation that can activity and maintain health.