Earticle

본연구에서는전통공연에서추임새로대표되는공연자와관객간의소통을인공지 능기반텍스트감정분석기술을사용하여구현하고자하였다. 텍스트정보를통해감 정의변화를알아내는인공지능알고리즘은감정의수치를도출하게되는데, 이수치 는다양한쓰임새가있지만, 우리는공연의한요소로사용하였다. 공연은특히우리 나라 관객문화 중 하나인 추임새를 모티브로 하므로, 진도 북춤으로 선정하였다. 선정된북춤은기본적으로Posenet과같은기술을통해동작인식등으로다양한 효과를줄수도있지만, 관객과의소통으로생겨난수치를통해그양과크기등을전 체 줄거리 흐름을 해치지 않는 선에서 변화를 주었다. 인공지능기술을활용한공연은앞으로한국의공연문화를알라고또한코로나시 대에 걸맞은 공연으로 발전하는데 이바지할 그것으로 예상한다.

In this study, communication between the performer and the audience, represented by Chuimsae in traditional performances, was implemented using artificial intelligence-based text emotion analysis technology. An artificial intelligence algorithm that detects changes in emotion through text information derives a numerical value of emotion, which has various uses, but we used it as an element of the performance. The performance was inspired by Chuimsae, one of the audience culture in Korea. The selected performance, Jindo Buk Chum, can basically give various effects such as motion recognition through techniques such as Posenet, but the quantity and size of the selected drum dance were changed in a line that does not impair the flow of the whole story through the figures generated through communication with the audience. It is expected that performances using artificial intelligence technology will contribute to the development of performances suitable for the corona era as well as to know Korea’s performance culture in the future.

본 연구는 빅 데이터를 활용하여 의료 서비스 이용자들의 온라인 정보 탐색에 관한 내용을 분석하였다. 분석 결과 ‘진료 후기’에 대한 정보 탐색 빈도가 높은 것으로 나타났다. 감정 분석은 온라인 정보 탐색에 대해 긍정적인 인식(63.7%)이 더 높은 것으로 나타났다. 토픽 모델링은 ‘후기’가 모든 토픽에서 공출현하여 핵심 주제로 나타났다. Concor(담론) 분석은 소비자들의 정보 탐색 범위가 반려동물 의료 서비스까지 범위가 확대되어 해당 시장이 성장하고 있으며, 코로나19가 정신 건강에 미치는 영향이 높 고 밀접한 관계가 있는 것으로 나타났다. 본 연구 결과는 의료 마케팅 담당자에게 ‘진료 후기’ 제공에 중점을 두어야 하고, 반려동물 의료 서비스에 대한 마케팅을 고려해야 함을 시사한다. 마지막으로 빅 데이터를 활용한 의료 서비스 정보탐색에 관한 연구가 부족한 부분에 대해 본 연구는 해당 분야를 확장 하는데 기여한다.

This study analyzed the contents of online information search by medical service users using big data. As a result of the analysis, it was found that the frequency of searching for information on 'Medical review' was high. Emotion analysis showed a higher positive perception(63.7%) of online information search. Topic modeling appeared as a key topic with 'review' co-emerging in all topics. Concor analysis showed that consumers' information search has expanded to pet medical services, and that the market is growing, and that COVID-19 has a high impact on mental health and is closely related. The results of this study suggest that the focus should be on providing 'Medical review' to healthcare marketers and that marketing to pet healthcare services should be considered. Finally, this study contributes to expanding the field of research on medical service information search using big data.

When developing a software engineering project, selecting the most appropriate programming language is a crucial step. Most often, feeling at ease with the possible options becomes almost as relevant as the technical features of the language. Therefore, it appears to be worth analyzing the role that the emotional component plays in this process. In this article, we analyze the trend of the emotions expressed by developers in 2018 on the Stack Overflow platform in posts concerning 26 programming languages. To do so, we propose a learning model trained by distant supervision and the comparison of two different classifier architectures.

In light of the hate crimes directed at the AAPI community during the coronavirus pandemic, a shooting in Atlanta, Georgia, in 2021 resulted in the deaths of six women of Asian descent. Following the shooting, a surge of tweets emerged with the hashtag #StopAsianHate, expressing a wide range of emotions and sentiments in response to both the shooting and the hate crimes that have occurred since the outbreak of COVID-19. The findings of the analysis reveal that negative sentiment consistently drives retweets on Twitter, underscoring the pivotal role of negative emotions in shaping tweet virality. However, positive emotions, particularly joy and fear, also demonstrate positive associations with retweets in specific instances, highlighting a nuanced relationship between emotional expressions and shareability. In summary, while negative emotions exert a significant impact, positive emotions-particularly joy and fear-also show positive correlations with retweet counts in certain contexts. This indicates that both negative and positive emotional expressions contribute to tweet shareability, although their effects vary depending on the specific emotion and situational context. Our findings provide significant insights into the literature on social media engagement, emphasizing the need for a nuanced understanding of user behavior and the intricate mechanisms governing online interactions. Future studies should investigate whether these findings are unique to AAPI members or applicable to the broader topic of racial hate crimes.

현재 존재하는 인공지능 기반 음악 생성에 관한 여러 모델과 연구는 수동 텍스트(Text) 기반 음악 생성에 대해 다루고 있다. 본 논문은 사용자의 편의성을 높이고, 창의적인 음악 생성 과정 을 더욱 원활하게 할 수 있도록 텍스트(TEXT) 프롬프트(Prompt) 자동화를 통한 음악 생성시 스템 방안을 제안한다. 그 방안으로 음원 파일을 통해 수집한 음악 분석 및 데이터화와 가사 정보에서 추출한 키워드를 기반한 장르, 가수, 앨범 등의 정보가 포함된 데이터셋(Dataset)을 구축 후, 파이썬(Python)의 자연어 처리 방법인 Konlpy를 사용하여 가사 데이터를 토큰화하고, TF-IDF(Term Frequency-Inverse Document Frequency) 벡터화를 통해 중요한 단어를 추 출한다. 또한, MFCC, 템포 등의 특징 데이터셋을 통하여 모델을 통한 감정을 예측하고, CNN 모델 및 Chatgpt를 활용한 텍스트 프롬프트를 자동생성하는 방법을 구현하여, MusicGen 모델 을 사용한 자동화 생성 프롬프트 기반 음악을 생성한다. 본 텍스트 프롬프트 자동 생성 화를 통한 음악 생성 연구의 결과는 음악 데이터 분석 및 생성 분야에 기여될 것으로 기대한다.

Current AI-based music generation models and research primarily focus on manual text-based music generation. This paper proposes a music generation system that automates text prompts to enhance user convenience and streamline the creative process. The study involves building a dataset that includes genre, artist, and album information by analyzing and processing music data collected from audio files and extracting keywords from lyrics. The lyrics data are tokenized using the KoNLPy natural language processing library in Python, and key terms are extracted through TF-IDF vectorization. Additionally, the study suggests a method for automatically generating text prompts using MFCC, tempo, and other feature data to predict emotions through a model that combines CNN and ChatGPT. These automatically generated text prompts are then input into the MusicGen model to automatically create new music that reflects the user's emotional state and musical preferences. The findings of this study are expected to contribute to the field of music data analysis and generation.

As the initial form of human civilization, games are symbolic interactive behaviors full of emotional communication. With the development of technology, mobile online games have become a new mass entertainment and social media and profoundly changed people's communication mode. Mobile online games have inherited the emotional interaction behavior in traditional games, but also opened up a new emotional interaction mode, reshaping modern people's cognition and communication. Players establish contact with other players through games to produce interactive behaviors, and the essence of interactive behaviors is the process of emotional communication. The rapid development of mobile online games has not only changed the development of social economy, but also changed the economic and social relations between people and society, as well as the emotional cognitive style. This paper holds that the essence of any interactive behavior is the process of emotional communication, which will have a far-reaching impact on society and individuals. Studying gamer and society under modern conditions from the perspective of emotional communication may better understand our life and existence. On the other hand, in the process of emotional interaction, the inevitable conflict causes one or more negative emotions to breed, which may gradually develop into serious social problems, especially adolescent health problems. Therefore, how to make better use of the positive effects of emotional communication in mobile online games in real life, work and study, and how to avoid and solve the social and personal problems that may arise in the process of emotional communication in mobile online games are practical problems that need to be further considered and solved in future research.

Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.

This research proposes the development of an online psychological counseling platform utilizing an Artificial Intelligence (AI)-based emotion analysis system. The platform, leveraging facial video, voice, and text data, aims to real-time identify and analyze the emotional states of counselees in a non-face-to-face counseling environment, providing counselors with the necessary information to facilitate appropriate counseling. The outcomes of this research are expected to enhance communication effectiveness between counselors and counselees and contribute to the psychological well-being of counselees in online counseling scenarios.

The purpose of this study is to investigate the emotion effects on academic achievement for university students. The results are as follows. Resulting on the each emotions difference by the statistical variables, anxiety scores by gender showed a significant difference in the p<.01 level(F=7.685). The males anxiety(2.478, standard deviation: 0.180) had significantly lower scores than females(3.076, standard deviation: 0.168). But fear, anger, activity, and sociability scores were not significantly different respectively between male and female students. To see the emotions effect of academic achievement, the analysis method of the linear regression line was used. As the result, anxiety, fear, anger, activity, and sociability did not significantly influence academic achievement. And so unlike previous methods, the analysis method of the quadratic regression curve was used. As the result, anxiety, fear, anger, activity, and sociability showed did significantly influence academic achievement respectively within 5% of statistical significance level, to more than F=3.06. Therefore, the values on academic achievement of the each anxiety, fear, anger, activity, and sociability showed a quadratic regression curve. That is, [Academic achievement]=-0.9685×[Anxiety]2+5.1342× [Anxiety]+8.2679,[Academic achievement]= -1.0638×[Fear]2+5.5694×[Fear]+7.5635,[Academic achievement]=-1.3497×[Anger]2+9.1284× [Anger]+0.6720,[Academic achievement]=-1.0589×[Activity]2+7.4386×[Activity]+1.8272,[Academic achievement]=-1.6830× [Sociability]2+11.2325× [Sociability]-3.8258. Therefore, we were able to determine the following conclusions. First, we were able to predict the degree of academic achievement by the each emotions scale. Second, when the each emotion scores of students was a moderate, the academic achievement was most excellent. So, in order for the students to become higher academic achievement, the maintenance of medium degree of the each emotions scores is required.

In this paper, we present facial expression recognition using feature-adaptive motion analysis and ID3 decision tree. Our method optimized the information gain heuristics of ID3 and consists of three parts: (1) simple geometric face model representation, (2) effective facial feature extraction using feature selection and feature-adaptive motion analysis, and (3) computationally inexpensive rule-based classification using ID3 tree. Our methods avoids complicated face model representation such as 3D modeling of human face, rather it uses FAC5-alike but much simpler face model by using fewer features and their actions. Feature selection and feature-adaptive motion analysis estimate motion patterns in fast and effective manner by assigning computational complexity on necessary parts only. Moreover, information gain heuristics of ID3 tree forces the classification to be done with minimal Boolean comparison. The performance was acceptable with overall recognition accuracy of 77% for JAFFE database (95 expressed images).

Color has become a fundamental element in contemporary visual arts, significantly influencing emotional resonance and narrative depth. Alberto Mielgo, an acclaimed animator and director, has gained international recognition for his innovative use of color and unconventional visual storytelling, particularly in digital animation. This study examines Mielgo's representative animated works—The Witness, The Windshield Wiper, and Jibaro—from a colorological perspective, analyzing how his distinctive chromatic strategies enhance narrative structure and emotional engagement. Specifically, The Witness employs extreme complementary contrasts of neon colors within urban environments to evoke simultaneous feelings of anxiety and allure. Conversely, The Windshield Wiper utilizes muted pastel palettes with subtle shifts in brightness and saturation to convey introspective emotions characteristic of modern urban life. Lastly, Jibaro integrates symbolic chromaticism through metallic gold tones contrasted against serene natural hues, visually allegorizing themes of greed, toxic relationships, and colonial violence. Through these analyses, this paper highlights how Mielgo's sophisticated integration of traditional color theory with contemporary digital technologies expands the expressive possibilities of animation. Ultimately, this research underscores the critical role of color as more than mere aesthetic decoration; rather, it functions as a powerful narrative device capable of profoundly shaping viewer perception and emotional immersion. The findings further suggest meaningful implications for future creative practices across diverse digital media platforms including animation, gaming, VR (Virtual Reality), and XR (Extended Reality).

To explore effects on each the emotional awareness, emotional expression, emotional empathy, and emotional regulation, of the sub-factors of the child's emotional intelligence, to mural preference, emotion cultivation, and community connection, the hierarchical multiple regression analyses are performed(as in Table 1, 2, and 3). As the results, we found the following facts. Children's mural preference, emotion cultivation, and community connection were expressed by the following equations in order, respectively. Mural Preference = .170×[Emotional Awareness] (t=2.118, p=.036*) − .025×[Emotional Expression](t=-.275, p=.783) + .088×[Emotional Empathy](t=.938, p=.350) + .139× [Emotional Regulation] (t=1.529, p=.128). Mural Emotion Cultivation = -.021×[Emotional Awareness](t=-.294, p=.769) − .205× [Emotional Expression]( t=-2.573, p=.011*) + .265×[Emotional Empathy](t=3.156, p=.002*) + .192×[Emotional Regulation] (t=2.361, p=.019*). Mural Community Connection = −.001×[Emotional Awareness](t=-.007, p=.995) − .132×[Emotional Expression](t=-1.478, p=.141) + .172×[Emotional Empathy](t=1.732, p=.027*) + .098×[Emotional Regulation](t=1.072, p=.285).

색채 심리학은 색채와 관련된 인간의 행동을 연구하는 심리학의 한 분야이다. 색채는 상징성과 이미지를 지니는 동시에 인간과 심리적 교감을 나눈다. 각 색채는 희망, 열정, 사랑, 생명, 죽음 등 다양한 이미지를 갖고 있다. 여기에 각 색깔이 주는 독특한 자극은 인간의 감성과 심리에 큰 영향을 미치고 있다. 따라서 본 논문에서는 사용자가 방 모델링을 하고 그 모델링에 사용된 색상 정보를 분석하고 퍼지 소속 함수를 적용하여 소속도를 구한 후, 그 소속도를 퍼지 추론 규칙에 적용하여 각 색채의 빈도수를 백분율로 나타낸 후 그 색채에 따른 인간의 감성 상태를 파악하는 기법을 제안하였다. 제안된 색체 기반 감성 분석 기법을 알슐러와 해트윅(Alschuler and Hattwick)의 색채에 따른 감성 상태에 적용한 결과, 제안된 기법이 효율적인 것을 확인하였다.

Psychology of color is a research field of psychology for studying human's behavior connected with color. Color carries symbolism and image while sharing psychological consensus with human. Each color has a respective image such as hope, passion, love, life, death, and so on. Peculiar stimuli by colors on these images have great influence on human's emotion and psychology. We therefore proposed a method for understanding human's state of emotion based on colors in this paper. In order to understand human's state of emotion, we analyzed color information used to model a room by a user and then described frequencies of each color as percent using fuzzy inference rules by membership values of fuzzy membership functions for colors used for modeling the room. When we applied the proposed color-based emotion analysis method to emotional state based on colors of Alschuler and Hattwick, we could see the proposed method is efficient.

감정은 서사 생성과 이해 모두에서 중요한 역할을 한다. 본 논문은 플루칙의 감정 모델을 기반으로 영화 대본에서 8가지 감정 표현을 분석하였다. 먼저 각 장면별 수동으로 감정을 태깅하였고, 이 때 8가지 감정 중 분노, 공포, 그리고 놀람이 가장 우세하게 나타났는데, 이는 스릴러 영화 장르를 고려할 때 의미있다고 할 수 있다. 또한, 스토리에서 긴장이 가장 고조되는 클라이맥스에서 다양한 감정이 복합적으로 나타난다고 가정하였고, 대본 상에서 3 부분의 클라이맥스를 확인할 수 있었다. 그 다음으로 파이썬 (Python) 프로그래밍 언어 기반 자연어처리 도구인 NLTK (Natural Language ToolKit)의 감성 분석 도구를 이용하여 수동 감정 태깅과 비교한 결과, 분노와 공포 감정에서 높은 일치율을, 그리고 놀람, 기대, 혐오 감정에서는 낮은 일치율을 보임을 확인하였다.

Emotion plays a key role in both generating and understanding narrative. In this article we analyzed the emotions represented in a movie script based on 8 emotion types from the wheel of emotions by Plutchik. First we conducted manual emotion tagging scene by scene. The most dominant emotions by manual tagging were anger, fear, and surprise. It makes sense when the film script we analyzed is a thriller-genre. We assumed that the emotions around the climax of the story would be heightened as the tension grew up. From manual tagging we could identify three such duration when the tension is high. Next we analyzed the emotions in the same script using Python-based NLTK VADERSentiment tool. The result showed that the emotions of anger and fear were most matched. The emotion of surprise, anticipation, and disgust, however, scored lower matching.

본 연구는 후기청소년의 정서경험 유형별 색채정서 차이를 밝히고 미술치료에서 색채정서 지표로 활용하는 데 그 목적이 있다. 이를 위해 국내 후기청소년 410명을 구글 웹 기반 모바일 설문시스템을 활용하여 네 개의 정서경험 유형(격렬, 둔감, 압도, 안정)으로 분류한 후 12개 기본색에 대한 색채정서를 비교하고 분석하여 결과를 도출하였다. 연구 결과 네 유형 모두 빨강, 보라, 갈색, 회색, 검정은 부정정서, 주황, 노랑, 연두, 초록, 분홍, 하양은 긍정정서가 나타났다. 유일하게 파랑은 ‘격렬’, ‘압도’ 유형이 긍정 정서를, ‘둔감’, ‘안정’ 유형은 부정정서를 보였다. 유형 간 유의한 차이는 다음과 같다. ‘격렬’, ‘압도’ 유형은 파랑에서 보다 높은 긍정정서를, ‘둔감’ 유형은 주황, 노랑, 연두에서 보다 낮은 긍정정서가 나타났다. ‘격렬’ 유형은 갈색, 검정에서 보다 높은 부정정서를, ‘압도’ 유형은 검정에서 보다 높은 부정정서가 나타났다. ‘격렬’ 유형은 유채색, 원색, 난색에서 보다 높은 긍정정서를, ‘둔감’ 유형은 보다 낮은 긍정정서가 나타났다. 한편 ‘안정’ 유형은 난색과 한색을 각각 긍정, 부정정서로 느끼는 반면, 그 외 유형은 이러한 차이가 나타나지 않는 모호한 색채정서 경향을 보였다. 또한 ‘안정’ 유형은 주황과 파랑을 각각 긍정, 부정정서로 느끼지만, 그 외 유형은 이러한 차이가 나타나지 않는 모호한 색채정서 경향이 나타났다. 이러한 내용을 바탕으로 본 연구의 의의와 제한점에 대하여 논의하였다.

This study purposed to reveal differences in color emotions according to the emotional experience types of late-stage adolescents and use them as color emotion indicators in art therapy. To this end, 410 late teenagers in Korea were classified into four types of emotional experience(hot, cold, overwhelmed, cerebral) using a Google web-based mobile survey system, and the results were derived by comparing and analyzing the color emotions for 12 colors. As a result of the study, red, purple, brown, grey, and black showed negative emotions in all four types, while orange, yellow, yellow-green, green, pink, and white showed positive emotions. Only blue showed positive emotions in the ‘hot’, and ‘overwhelmed’ type, while the ‘cold’, and ‘cerebral’ types showed negative emotions. The ‘hot’, and ‘overwhelmed’ types showed higher positive emotions in blue, while the ‘cold’ types showed lower positive emotions in orange, yellow, and yellow-green. The ‘hot’ type showed higher negative emotions in brown and black, and the ‘overwhelming’ type showed higher negative emotions in black. The ‘hot’ type showed higher positive emotions in chromatic, primary, and warm colors, while the ‘cold’ type showed lower positive emotions. The ‘cerebral’ type represented warm and cold colors, orange and blue as positive and negative emotions, while other types showed ambiguous color emotions. Based on these contents, the significance and limitations of this study were discussed.

본 연구에서는 녹시율(綠視率, Index of Greenness)의 정서증진효과에 대한 회복환경 모델(이승훈, 2007a)을 “집" 혹은 “집 이외의 장소"라는 맥락에 따라 검증해 보고자 하였다. 대학생 참여자들(301명)은 하루 동안 12회에 걸쳐 휴대전화 문자메시지를 전송 받았다. 문자메시지를 받자마자 참여자들은 “지금 이 순간"을 기준으로 자신이 있는 곳의 사진을 한 장 촬영하였고, 자신이 있는 곳이 “집"인지 혹은 “집 이외의 장소"인지를 표시하였으며, 이어서 스트레스 척도, 회복환경지각척도, 정적 정서 및 부적 정서 척도를 평정하였다. 일련의 데이터 처리 과정을 거쳐, 표본 1(n = 233; 집 혹은 집 이외의 장소에서 식물의 잎을 본 경우의 데이터들을 모두 포함시킨 표본), 표본 2(n = 54; 집에서 식물의 잎을 본 경우의 데이터들만 포함시킨 표본), 표본 3(n = 219; 집 이외의 장소에서 식물의 잎을 본 경우의 데이터들만 포함시킨 표본)을 만들었다. 회복환경 모델을 표본 1, 2, 3을 대상으로 검증하고 비교해 보았다. 경로분석 결과 (1) 적합도 지수는 표본 3에서 가장 높았고, (2) ‘녹시율 → 회복환경' 경로는 표본 3에서만 유의하였으며, (3) ‘회복환경 → 정서' 경로는 세 표본 모두 유의하였다. 위계적 회귀분석을 한 결과 표본 2에서만 스트레스 × 회복환경 상호작용이 유의하게 나타났다. 즉, 정서에 부정적인 영향을 미치는 스트레스의 효과가 회복환경 지각이 높을 때에만 두드러지게 나타났다. 본 연구 결과는 녹시율이 낮음에도 불구하고 집이 심리적 회복의 중요한 세팅 역할을 하고 있으며, 이로 인해 정서를 증진시킴을 보여준다. 이와는 대조적으로, 본 연구는 집 이외의 장소에서는 녹시율이 높을수록 심리적 에너지가 잘 회복되며, 이로 인해 정서가 증진됨을 보여준다.

This study is to test the restorative environment model (Lee, 2007a) of the emotion-improving effects of the Index of Greenness (IG) according to the context of “home" or “places other than home." During a day, cellular phone text messages were sent to South Korean undergraduates (n = 301) twelve times. On receiving each message, participants took a photograph of their surroundings, marked if they were either “at home" or “in places other than home", and then rated stress scale, the Perceived Restorativeness Scale (PRS), and Positive Affect and Negative Affect Schedule (PANAS) with reference to “here and now." Three samples were drawn through a series of data processing. These samples consisted of the data only in case that the participants saw the leaves of plants (1) at home or in places other than home (sample 1; n = 233); (2) at home (sample 2; n = 54); and (3) in places other than home (sample 3; n = 219). The restorative environment model was tested with sample 1, 2, and 3, and then compared. A series of path analyses showed that: (1) the fit indices were better for the data from the sample 3 than the sample 1 and 2; (2) the causal path from ‘IG' to ‘restorative environment' was statistically significant only in sample 3; and (3) the causal paths from ‘restorative environment' to ‘emotion' were statistically significant in all three samples. Hierarchical multiple regressions showed that stress × restorative environment interaction was statistically significant only in sample 2. In other words, the negative effect of stress on emotion was present only at high level of restorative environment. These results indicate that in spite of the low level of IG, home is an important setting for psychological restoration, which enhances emotion. In contrast, these results indicate that in places other than home, higher IG is associated with higher psychological restoration, which enhances emotion.

본 연구의 목적은 로샤 검사를 통하여 신체화 집단의 감정표현불능증과 대처손상 경향을 검토하고, 그 결과를 인지, 정서, 행동 및 스트레스 관리 측면에서 분석하는 것이다. 이러한 목적을 위하여, 56명의 신체화 집단이 두 가지 규준 자료(한국 정상성인과 미국 정상성인의 규준)와 비교되었다. 본 연구결과에서 신체화 집단은 비교집단들에 비하여 전체반응수(R), 인간 반응수(M)가 적었고, 색채반응 총점수(W Sum C)가 낮았다. 또한 신체화 집단은 비교집단들에 비하여 혼합반응수가 적었고, Lambda 점수가 높았으며, 경험실제(EA) 점수가 작았다. 아울러 신체화 집단은 미국 정상성인에 비하여 형태-색채(FC) 반응수가 적었다. 이러한 결과들은 신체화 집단의 감정표현불능증을 암시한다. 게다가 신체화 집단은 심한 대처손상을 나타냈다:(1) EA<6, (2) COP<2, AG<2, (3) W Sum C<2.5, Afr<.46, (4) Pure H<2가 신체화 집단에서 대부분 나타났고, (5) Isolation Index>.24도 신체화 집단 일부에서 나타났다. 마지막으로, 결과가 인지, 정서, 행동 및 스트레스 관리 측면에서 분석되었다. 이러한 분석을 토대로, 치료에 대한 권고가 제안되었다.

The aims of this study were (a) to examine the alexithymic syndrome and coping deficits in somatization group through the Rorschach test and (b) to analyze the results in the aspects of the cognition, emotion, behavior, and stress management. For these purposes, we compared 56 somatization patients with the two normative data: Korean adult norms of Rorschach response provided by Kyung-Jin Shin and Ho-Teak Won(1991) and American those provided by Exner(1990). The results showed that somatization patients had smaller number of R, fewer M responses, lower W Sum C score, fewer blends responses, higher Lambda score and lower EA score than did comparison groups. Also, somatization group showed fewer FC responses than did American adults. These results indicated the alexithymic syndrome in somatizaion group. Besides, somatization patients had severe coping deficits: (1) EA<6, (2) COP<2, AG<2, (3) W Sum C<2.5, Afr<.46, (4) Pure H<2 were appeared in most of somatization patients and (5) Isolation Index>.24 in some of somatization patients. Finally, the results were analysed in the aspects of the cognition, emotion, behavior, and stress management. Based on these analyses, recommendations for treatment were suggested.

본 연구에서는 인간에게 공감할 수 있는 AI모델을 구축하기 위해 수집된 대규모 COVID-19 감정 일기 데이터를 기반으로, 제3자가 분류한 조망수용수준과 심리적, 정서적 특성의 관계를 확인하였다. 먼저 연구참여자 5,403명의 심리적 특성을 측정하기 위해 활용된 단축형 자기보고식 척도의 신뢰도를 측정하였다. 그 결과 신경과민성, 우호성, 외로움, 공감, 지각된 스트레스의 문항이 일부 선별되었다. 이후, 군집분석을 통해 연구참여자를 세 개의 조망수용 군집으로 나누고 각 군집간 심리적 특성의 차이를 살펴보았다. 높은 조망수용능력 군집은 낮은 조망수용능력 군집보다 높은 수준의 우호성, 공감 수준을 보였고, 보다 낮은 일기스트레스와 낮은 부정정서 수준을 나타냈다. 낮은 조망 수용능력 군집은 세 집단 중 가장 높은 수준의 신경과민성, 외로움, 지각된 스트레스, 일기스트레스, 그리고 높은 부정정서 수준을 보였다. 조망수용능력이 확인되지 않은 미결정 군집은 가장 낮은 신경과민성, 외로움, 지각된 스트레스와 부정정서 수준을 보였다. 미결정 집단을 제외한 조망수용수준과 심리적, 정서적 특성의 관계는 선행연구에서 시사된 바와 일치하였다. 마지막으로 논의에서는 본 연구의 의의와 빅데이터를 활용한 정신건강개입 발전 가능성에 대한 시사점을 제시하였다.

Using large data collected during COVID-19, the present study explored how perspective-taking levels of diary writers evaluated by the third party were associated with their psychological characteristics. We analyzed the perspective-taking levels of the 5,403 participants who wrote diary entries on their thoughts and emotions, along with their self-reported psychological characteristics. Cluster analysis revealed three distinct perspective-taking profiles. We examined differences in psychological traits between groups. The higher perspective-taking group exhibited higher agreeableness, higher trait empathy, lower diary stress, and less unpleasant feelings than the lower perspective-taking group. Neuroticism, loneliness, perceived stress, diary stress, and unpleasant feelings were the highest in the lower perspective-taking group. Neuroticism, loneliness, perceived stress, and unpleasant feelings were the lowest in the undetermined group. Our findings mirrored the previously documented links between self-reported perspective-taking abilities and psychological qualities. Applications for large-scale text-based mental health therapies were also discussed in this study.

본 연구는 정서 스트룹 과제와 몰핑 기법을 이용한 얼굴표정 정서 인식 과제를 수행하여 자살 생각 경향 집단의 정서처리 특성과 실패 처치 조건에서 정서처리 특성을 검토하고자 하였다. 구체적으로, 자살 생각 경향 집단이 통제 집단과 비교하여 정서 차이가 있는지 정서 스트룹 과제와 얼굴표정 정서 인식 과제를 실시하여 알아보고자 하였다. 그리고 실패 경험을 제시하여 자살 생각 경향 집단이 통제 집단과 정서처리 특성에서 차이가 있는지 정서 스트룹 과제와 얼굴표정 정서 인식 과제를 통해 알아보고자 하였다. 자살 생각 경향 집단과 통제 집단의 반응시간을 비교한 결과, 정서 스트룹 과제에서 자살 생각 경향 집단은 통제 집단과 비교하여 부정 및 긍정 정서 편향이 통계적으로 유의하였으며, 얼굴표정 인식 과제에 의한 정서 인식 민감도에서 집단 간 주효과 및 정서 유형 간 주효과가 통계적으로 유의하였다. 실패 경험을 제시한 후 반응시간을 비교한 결과, 정서 스트룹 과제에서 자살 생각 경향 집단은 통제 집단과 비교하여 부정 정서 편향이 통계적으로 유의하였으며, 얼굴표정 인식 과제에 의한 정서 인식 민감도에서 집단 간, 정서 유형 간 주효과와 집단과 정서 유형의 상호작용이 통계적으로 유의하였다. 즉, 자살 생각 경향 집단은 실패 경험에 의해 정서 인식 민감도의 역치가 낮아졌으며, 부정적 정서 편향이 나타났다. 따라서 환경적 스트레스 상황이 자살 생각 경향 집단의 정서에 영향을 미칠 수 있음을 확인할 수 있었다. 이러한 결과들을 종합하여 결과의 시사점과 제한점 및 추후 연구방향에 대하여 논의 하였다.

In this research, characteristics of emotional information processing of a group of people with a tendency to have suicidal ideation under normal and failure treatment conditions have been studied through emotional stroop and facial emotion recognition tasks with morphing techniques. In the concrete, the emotional stroop and facial emotion recognition tasks have been performed to confirm whether there is any difference in emotion between the group having tendencies of suicidal ideation and the control group. Furthermore, an analysis of whether there is any evidence of differences in emotion when presented with failure experiences between the group having tendencies for suicidal ideation and the control group was completed. The result of the comparison of reaction time tests between the group having tendencies for suicidal ideation and the control group revealed that the non-control group showed statistically significant positive and negative emotional biases in the emotional stroop, and also displayed a statistically significant main effect in the sensitivity level between groups and emotional types for the recognition of emotion by a facial recognition task. In the comparison for the reaction time after presenting a failed experience, the non-control group indicated a significant negative-emotional bias in the emotional stroop task compared to the control group. The non-control group displayed a significant main effect between emotional types and groups, and the interactive effect between the emotional types and the groups. In conclusion, the group having tendencies for suicidal ideation showed a lowered level of threshold for emotional recognition sensitivity and displayed a negative emotional bias. Therefore, it is confirmed that environmental stress can affect the emotion of the group having tendencies of suicidal ideation. Synthesizing the implications from the study, further discussion on the issue and future research direction has been followed.