• The Korean Journal of Franchise Management
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• v.14 no.1
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• pp.1-21
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• 2023

Purpose: During COVID-19, consumers prefer social distancing or contactless activities for safety, and hygienic condition has become one of the most important factors in evaluating restaurants. Therefore, this study aims to investigate whether offline/online word-of-mouth is affected by restaurant quality. Research design, data and methodology: The data were collected from 480 consumers who had experiences of visiting a restaurant in the past 90 days and analyzed with SPSS 28.0 and SmartPLS 4.0 programs. Results: Physical environment and menu had positively significant effects on brand love, while employee service and hygiene had no significance on brand love. Restaurant environment, menu, and hygiene had negatively significant effects on brand hate, but employee service had not significant impact on brand hate. Brand love had positively significant effects on offline and online word-of-mouth, and brand hate had negatively significant effects on offline and online word-of-mouth. Conclusions: First, restaurants need to develop a pleasant space where customers can have emotional experiences. Second, restaurants need to fulfill customers' desire for global food consumption. Third, restaurants should ensure hygiene and safety to prevent customers' brand hate. Lastly, restaurants need to establish offline/online word-of-mouth strategy to identify which restaurant quality attributes influence brand love/hate and offline/online word-of-mouth.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.266-274
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• 2023

Recently, sterilization technology has received increasing interest due to the COVID-19 pandemic and required safety precautions. Particularly, sterilization devices using near ultraviolet (UV) with a 405 nm wavelength are also drawing attention. It has a UV-C wavelength and other sterilization effects. Its blue-colored light on the boundary between UV and visible light is used as a light-emitting diode (LED) lamp for 405 nm sterilization, owing to its longer wavelengths than UV rays. However, the 405 nm wavelength contains blue light that can damage the eyes and skin during prolonged exposures and affect the emotional and biological parts of the body. Currently, 405 nm sterilization LED light registers are circulating in the market. However, they have not undergone safety tests for blue-light hazards. Thus, with the active distribution of sterilization LED lights, solid safety standards and management systems are essential to protect users from blue-light hazards. Accordingly, in this study, we conducted spectral radiance and spectral radiative luminance tests on 405 nm sterilization LED registers available in the market by the measurement criteria of IEC 62471. Safety standards must be established to secure users' safety against blue light hazards at a time when 405nm sterilization LED lights are actively distributed due to COVID-19.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.6-8
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• 2022

Many systems that use images through object recognition using deep learning have provided various solutions beyond the existing methods. Many studies have proven its usability, and the actual control system shows the possibility of using it to make people's work more convenient. Many studies have proven its usability, and actual control systems make human tasks more convenient and show possible. However, with hardware-intensive performance, the development of models is facing some limitations, and the ease with the use and additional utilization of many unupdated models is falling. In this paper, we propose how to increase utilization and accuracy by providing additional information on the emotional regions of colors and objects by utilizing learning and weights from HSV color histograms of local image data recognized after conventional stereotyped object recognition results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.87-89
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• 2022

Recently, as more consumers spend more time at home due to COVID-19, the time spent on digital consumption such as SNS and OTT, which can be easily used non-face-to-face, naturally increased. Since 2019, when COVID-19 occurred, digital consumption has doubled from 44% to 82%, and it is important to quickly and accurately grasp and apply trends by analyzing consumers' emotions due to the rapidly changing digital characteristics. However, there are limitations in actually implementing services using emotional analysis in small systems rather than large-scale systems, and there are not many cases where they are actually serviced. However, if even a small system can easily analyze consumer trends, it will help the rapidly changing modern society. In this paper, we propose a lightweight trend analysis system that builds a learning network through Transfer Learning (Fine Tuning) of the BERT Model and interlocks Crawler for real-time data collection.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.211-219
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• 2023

In this study, the importance of each design element was analyzed by surveying experts in the development of Agro-Healing Virtual Reality Therapy System. It was found that the results of experts content importance were consistent with the results of consumer preferences in previous studies, such as psychological and emotional stability as the main effect the importance of sight and hearing, a relatively short time of 30 minutes or less, a low price of 5,000 won or less, technical factors that can satisfy the five senses, and various contents. When the spatial elements of the Agro-Healing Virtual Reality Therapy System were categorized into three major categories: elements and equipment, lines and paths, and sites and spaces, 'flowers', 'playgrounds', 'paths', 'sidewalks', 'rest areas' and 'gardens' were found to be highly important. Among the components of Agro-Healing Virtual Reality Therapy System, the usability was divided into eight major categories, including searchability, attractiveness, cognition, error handling, control, consistency, convenience, and feedback, and the importance was analyzed for each component. The significance of this study is that it suggests the design direction of virtual healing farm systems and provides effective information that can be used in the development of related systems in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.111-118
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• 2006

The purpose of the study is to examine the effects of the positive menial stress, eustress, on autonomic nervous system(ANS) and human health. For this, we analyzed heart rate variability(HRV) parameters, the most promising markers of ANS function to assess the changes of emotional and physiological states of human body. We measured HRV Signal of World Cup group(281 male subjects: $29.8{\pm}5.6yr$., 187 female subjects: $29.0{\pm}5.4yr$.) in two stadiums at least an hour before the game during '2002 FIFA World Cup Korea/Japan' event. We also measured control group's(331 male subjects: $30.9{\pm}4.7 yr$., 344 female subjects: $30.2{\pm}5.2 yr$.) in the health promotion centers in two university hospitals at least a month before and after the world cup event period. Considering physiological differences between males and females, the data analysis was applied to 'male group' and 'female group' separately. As a result, some tendency was observed that is different from what we have known as the stress reaction. In general, all parameter values except that of mean heart rate tend to decrease under stressed condition. However, under eustressed condition, both heart rate and standard deviation of the Normal to Normal intervals(SDNN) were higher then those of normal condition(p<0.05). Especially, in case of female group, contrary to distressed condition, every frequency-domain powers showed tile higher value(p<0.05, p<0.001). Considering that decrease of HRV indicates the loss of one's health, the increase of SDNN and frequency parameters means that homeostasis control mechanism of ANS is functioning positively. Accordingly, induction of eustress from international sports event may affect positively to the people's health.