• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.13-19
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• 2021

While watching the movie, the audience shows various emotional reactions. Emotional reactions such as sadness and anger, joy and anger appear depending on the storyline of the film. This aspect can be seen through the audience's brain wave response. This study is to examine the relationship between the movie story development and the movie story development through brain wave measurement of the emotional reaction of the audience in situations and events occurring in the movie development. Four American films, which represent each genre and are well known to many people, were selected for the study. These are of the adventure genre, of the animation genre, of the action genre, and of the drama genre. In order to measure the emotional response of these movies, four cases were set centered on the PPG of EEG and analyzed as a time series graph pattern. It can be seen that the emotional response on the graph has a certain relationship with the story development. It is expected that this study will help in selecting a genre when making a movie in the future, especially when deciding how to compose and develop a story, and it will help to induce the emotions of the audience.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1064-1070
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• 2020

In Korea, video-based news broadcasters are primarily classified into terrestrial broadcasters, general programming cable broadcasters and YouTube broadcasters. Recently, news broadcasters get subjective while targeting the desired specific audience. This violates normative expectations of impartiality and neutrality on journalism from its audience. This phenomenon may have a negative impact on audience perceptions of issues. This study examined whether broadcast news reporting conveys emotions and if so, how news broadcasters differ according to emotion type. Emotion types were classified into neutrality, happiness, sadness and anger using a convolutional neural network which is a class of deep neural networks. Results showed that news anchors or reporters tend to express their emotions during TV broadcasts regardless of broadcast systems. This study provides the first quantative investigation of emotions in broadcasting news. In addition, this study is the first deep learning-based approach to emotion analysis of broadcasting news.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.3
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• pp.237-244
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• 2021

Background: This study aimed to analyze Twitter users' emotional tendencies regarding oral surgery procedures before and after the coronavirus disease 2019 (COVID-19) pandemic worldwide. Methods: Tweets posted in English before and after the COVID-19 pandemic were included in the study. Popular tweets in 2019 were searched using the keywords "tooth removal", "tooth extraction", "dental pain", "wisdom tooth", "wisdom teeth", "oral surgery", "oral surgeon", and "OMFS". In 2020, another search was conducted by adding the words "COVID" and "corona" to the abovementioned keywords. Emotions underlying the tweets were analyzed using CrystalFeel - Multidimensional Emotion Analysis. In this analysis, we focused on four emotions: fear, anger, sadness, and joy. Results: A total of 1240 tweets, which were posted before and after the COVID-19 pandemic, were analyzed. There was a statistically significant difference between the emotions' distribution before and after the pandemic (p < 0.001). While the sense of joy decreased after the pandemic, anger and fear increased. There was a statistically significant difference between the emotional valence distributions before and after the pandemic (p < 0.001). While a negative emotion intensity was noted in 52.9% of the messages before the pandemic, it was observed in 74.3% of the messages after the pandemic. A positive emotional intensity was observed in 29.8% of the messages before the pandemic, but was seen in 10.7% of the messages after the pandemic. Conclusion: Infectious diseases, such as COVID-19, may lead to mental, emotional, and behavioral changes in people. Unpredictability, uncertainty, disease severity, misinformation, and social isolation may further increase dental anxiety and fear among people.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Journal of IKEEE
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• v.26 no.3
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• pp.430-436
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• 2022

In this paper, we propose an intelligent camera for multiple body temperature detection. The proposed camera is composed of optical(4056*3040) and thermal(640*480), which detects abnormal symptoms by analyzing a person's facial expression and body temperature from the acquired image. The optical and thermal imaging cameras are operated simultaneously and detect an object in the optical image, in which the facial region and expression analysis are calculated from the object. Additionally, the calculated coordinate values from the optical image facial region are applied to the thermal image, also the maximum temperature is measured from the region and displayed on the screen. Abnormal symptom detection is determined by using the analyzed three facial expressions(neutral, happy, sadness) and body temperature values. In order to evaluate the performance of the proposed camera, the optical image processing part is tested on Caltech, WIDER FACE, and CK+ datasets for three algorithms(object detection, facial region detection, and expression analysis). Experimental results have shown 91%, 91%, and 84% accuracy scores each.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.306-312
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• 2022

During the unprecedented COVID-19 outbreak, the public's information needs created an environment where they overwhelmingly consume information on the chronic disease. Given that news media affect the public's emotional well-being, the pandemic situation highlights the importance of paying particular attention to how news stories frame their coverage. In this study, COVID-19 news speech emotion from mainstream broadcasters in South Korea and the United States (US) were analyzed using convolutional neural networks. Results showed that neutrality was detected across broadcasters. However, emotions such as sadness and anger were also detected. This was evident in Korean broadcasters, whereas those emotions were not detected in the US broadcasters. This is the first quantitative vocal emotion analysis of COVID-19 news speech. Overall, our findings provide new insight into news emotion analysis and have broad implications for better understanding of the COVID-19 pandemic.

• Journal of People, Plants, and Environment
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• v.23 no.3
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• pp.277-291
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• 2020

Background and objective: The purpose of this study is to investigate the effects of visitors' positive emotions on satisfaction and loyalty with the perception of restorative environment of a healing garden created in an urban agriculture expo. Methods: The psychological indicators to the images of the healing garden were analyzed by the visitors' demographic variables and the three factors of plant cultivation activity level: plant cultivation experience, plant preference, and plant-related event. Results: Between age groups and occupational groups, significant differences were found statistically. The Perceived Restorativeness Scale(PRS) showed significantly differences between age groups in repose, fascination and legibility. The Positive Affect & Negative Affect Schedule(PANAS) showed statistically significant differences between age groups in positive emotions. In addition, we investigated the correlation between the PANAS and the three factors of plant cultivation experience level, the four factors of the PRS, satisfaction and loyalty. The three factors of plant cultivation experience level, the four factors of the PRS, satisfaction and loyalty showed a positive correlation with positive emotions and were inversely correlated with negative emotions significantly. Multiple regression analysis with dummy variables was conducted to examine the effects of plant cultivation activity level, attention restoration, and the PANAS on healing garden visitors' satisfaction and loyalty. As a result, among the four factors of the PRS, fascination and positive affectivity were significant variables that affect healing garden visitors' satisfaction and loyalty. Conclusion: The results indicated that the higher the attention restoration of visitors due to the fascination of the healing garden and the higher their positive affectivity and the more they have plant-related memories, the higher their impact on healing garden visitors' satisfaction and loyalty. Therefore, fascinating natural environments or greenery landscapes like healing gardens where people can contact plants would reduce negative emotions such as anger and sadness but to increase positive emotions such as pleasure, joy and satisfaction.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.71-77
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• 2022

In this paper, a database is collected for extending the speech synthesis model to a model that synthesizes speech according to emotions and generating facial expressions. The database is divided into male and female data, and consists of emotional speech and facial expressions. Two professional actors of different genders speak sentences in Korean. Sentences are divided into four emotions: happiness, sadness, anger, and neutrality. Each actor plays about 3300 sentences per emotion. A total of 26468 sentences collected by filming this are not overlap and contain expression similar to the corresponding emotion. Since building a high-quality database is important for the performance of future research, the database is assessed on emotional category, intensity, and genuineness. In order to find out the accuracy according to the modality of data, the database is divided into audio-video data, audio data, and video data.

• Journal of Oriental Neuropsychiatry
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• v.33 no.1
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• pp.79-111
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• 2022

Major depressive disorder (MDD) causes a persistent feeling of sadness and loss of interest. It can lead to emotional and physical problems. Treatments such as antidepressant and cognitive behavioral therapy for MDD have many limitations. Traditional East Asian Herbal Medicine (TEAM) is a representative modality of Complementary and Integrative Medicine (CIM) which can be used for MDD. However, no study has systematically reviewed the efficacy or safety of TEAM for MDD so far. Therefore, we performed a systematic review and meta-analysis to evaluate effectiveness and safety of TEAM as a monotherapy for MDD. We only included TEAM that could be used in context of clinical setting in Korean Medicine. Outcomes were the Hamilton Depression Rating Scale (HAMD) and total effective rate (TER). After comprehensive electronic search of 11 databases, we included 28 randomized controlled trials (RCTs) that compared HM as monotherapy with antidepressant for MDD. Meta-analysis showed that TEAM had significant benefits in reducing HAMD (MD=-0.40, 95% CI: -0.67 to -0.13, p=0.003, I²=85%) and improving TER (RR=1.06, 95% CI: 1.02 to 1.10, p=0.003, I²=0%). It also appeared to be safer than antidepressant in terms of adverse effects. Methods used for RCTs were poor and the quality of evidence was graded 'low' or 'moderate'. These findings indicate that the use of HM as a monotherapy might have potential benefits in MDD treatment as an alternative to antidepressant. However, considering the methodological quality of included RCTs, the clinical evidence is uncertain. Further well-designed RCTs are required to confirm these findings.