• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.604-621
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• 2021

Examination of the concurrent evolution of communication tools and eating behaviors over recent decades reveals that social media and other forms of digital content have become powerful new driving forces for nutritional choices and food consumption. The purpose of this research was to examine the effect between goal orientation of message (promotion versus prevention) and the type of message (text versus image) on effectiveness of the message. The findings showed that individuals exposed to a promotion-focused message similarly responded to the message regardless of the type of the message. By contrast, those who exposed to a prevention-focused message showed significantly more positive responses to the message posted on the text-based social media than the message on the image-based social media. The findings indicated that, if presented effectively, social media could be harnessed to promote healthier eating habits and behaviors, prevent those which can be harmful, and ultimately improve an individual's daily food consumption and overall quality of life.

• Journal of Industrial Convergence
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• v.19 no.3
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• pp.35-40
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• 2021

This study attempted to derive important factors of emerging infectious diseases by collecting and analyzing text data onto emerging infectious diseases. For this purpose, articles in the Naver News database were directly crawled, pre-processed, and used for data analysis. In addition, additional analysis was performed using Big Kinds. As a result of the priority analysis, the importance was shown in the order of corona, infectious disease, quarantine, vaccine, outbreak, virus, infection, and development. As a result of the proximity centrality analysis, the importance was shown in the order of government, death, and plan, and the analysis result of Big Kinds showed that Covid-19 and the Korea Centers for Disease Control and Prevention were important. Based on the results of this study, it can be said that the government's policy support is needed to raise public awareness of new infectious diseases, prevent disease, and develop vaccines and treatments.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.36-48
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• 2020

App market has continuously been growth since its launch. The market revenues will reach about 1,000 billion US dollars in 2019. App is a core service for smartphone. Currently, there are more than 1.5 million mobile apps in App platform calling out for attention. So, if you are looking at developing a successful app, you need to have a solid marketing and distribution strategy. Online word of mouth(eWOM) is one of the most effective, powerful App marketing method. eWOM affect potential consumers' decision making, and this effect can spread rapidly through online social network. Despite the increasing research on word of mouth, only few studies have focused on content analysis. Most of studies focused on the causes and acceptance of eWOM and eWOM performance measurement. This study aims to content analysis of mobile apps review In 2013, Google researchers announced Word2Vec. This method has overcome the weakness of previous studies. This is faster and more accurate than traditional methods. This study found out the relationship between mobile app reviews and checked for reactions by Word2vec.

• Smart Media Journal
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• v.10 no.1
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• pp.63-69
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• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.47-53
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• 2021

With increasing interest in the 4th industrial revolution, data-driven scientific methodologies have developed. However, there are limitations of data collection in the real estate field of research. In addition, as the public becomes more knowledgeable about the real estate market, the qualitative sentiment comes to play a bigger role in the real estate market. Therefore, we propose a method to collect quantitative data that reflects sentiment using text mining and k-means algorithms, rather than the existing source data, and to predict the direction of housing index through artificial neural network learning based on the collected data. Data from 2012 to 2019 is set as the training period and 2020 as the prediction period. It is expected that this study will contribute to the utilization of scientific methods such as artificial neural networks rather than the use of the classical methodology for real estate market participants in their decision making process.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.91-99
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• 2022

The shipping industry is a service industry that operates its business by transporting cargo on ships and receiving freight. Therefore, large-scale capital investment is required for ship operation, and if the value of the ship is uncertain, the risk of shipping management increases. This study aims to identify the factors affecting changes in ship value and to analyze the importance of each variable. To achieve the goal, the factors affecting changes in ship value were identified and structured using the techniques of text mining and topic modeling, and classified into three main factors and 12 sub-factors. This study used AHP analysis to examine the relative importance of each factor. Results indicated that the main factor influencing the change in the vessel value was the shipping factor, followed by the investment factor and the environment factor. Other auxiliary factors that substantially affect the ship value include the volatility of the shipping market and of shipping freight.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.205-222
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• 2022

This study aims to improve the information service so that construction engineers and construction project participants without specialized knowledge can easily understand the important words and the interrelationships between them in construction practice. To this end, using text mining and network centrality, the frequency of occurrence of words, topic modeling, and network centrality in construction practice information such as technical information, case information, and cost reduction, which are most used in the Construction Technology Digital Library, were analyzed. Through this analysis, design, construction, project management, specifications, standards, and maintenance related to road construction such as roads, pavements, bridges, and tunnels were identified as important in construction practice. In addition, correlations were analyzed for words with high importance by measuring Degree Centrality and Eigenvector Centrality. The result was that more useful information could be provided if the technical information was expanded. Finally, we presented the limitations of the study results and additional studies according to the limitations.

• Knowledge Management Research
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• v.23 no.2
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• pp.37-60
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• 2022

The growth of virtual reality games and the popularization of blockchain technology are bringing significant changes to the formation of the metaverse industry ecosystem. Especially, after Meta acquired Oculus, a VR device and application company, the growth of VR-based metaverse services is accelerating. In this study, the concept that supports leisure activities in the metaverse environment is explored realting to game-like features in VR apps, which differentiates traditional mobile apps based on a smart phone device. Using exploratory text mining methods and network analysis approches, 241 apps registed in the Oculus Quest 2 App Store were analyzed. Analysis results from a quasi-network show that a leisure concept is closely related to various genre features including a game and tourism. Additionally, the anlaysis results of G & F model indicate that the leisure concept is distictive in the view of gateway brokerage role. Those results were also confirmed in LDA topic modeling analysis.

• Smart Media Journal
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• v.12 no.7
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• pp.76-83
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• 2023

This study proposes a method to assist webtoon artists in the process of webtoon creation by utilizing a pretrained Text-to-Image model to generate webtoon images from text. The proposed approach involves fine-tuning a pretrained Stable Diffusion model using a webtoon dataset transformed into the desired webtoon style. The fine-tuning process, using LoRA technique, completes in a quick training time of approximately 4.5 hours with 30,000 steps. The generated images exhibit the representation of shapes and backgrounds based on the input text, resulting in the creation of webtoon-like images. Furthermore, the quantitative evaluation using the Inception score shows that the proposed method outperforms DCGAN-based Text-to-Image models. If webtoon artists adopt the proposed Text-to-Image model for webtoon creation, it is expected to significantly reduce the time required for the creative process.