• Title, Summary, Keyword: Social Emotion Detection

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Jointly Image Topic and Emotion Detection using Multi-Modal Hierarchical Latent Dirichlet Allocation

  • Ding, Wanying;Zhu, Junhuan;Guo, Lifan;Hu, Xiaohua;Luo, Jiebo;Wang, Haohong
    • Journal of Multimedia Information System
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    • v.1 no.1
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    • pp.55-67
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    • 2014
  • Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

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The Complex relationship between employment stress and avoidance coping styles for college students (대학생들의 취업스트레스와 회피대처방식의 융복합적인 관련성)

  • Kim, Mee-Jung
    • Journal of Digital Convergence
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    • v.17 no.3
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    • pp.353-360
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    • 2019
  • The purpose of this study was to investigate the relationship between job stress and coping style in college students. Participants were 314 students in a college. Data were collected using a self administered questionnaire. The survey was conducted from May 02, 2018 to May 28, 2018. There were statistically significant correlations between personality stress, family environmental stress, academic stress, school environment stress and emotion - centered coping style among sub - variables of job stress, Job anxiety stress was significantly correlated with social support seeking and emotion - centered coping style. Since college students' emotional stress coping style is related to depressive emotional and physical health problems, it is necessary to provide a psychological treatment program for early detection and coping with psychological support services, and a mixed service such as education, lecture, and camp. In addition, it is thought that strategic action skill training (plan, method, and technology) is needed to change from emotion - centered coping style to problem - solving style.

Recognition and Generation of Facial Expression for Human-Robot Interaction (로봇과 인간의 상호작용을 위한 얼굴 표정 인식 및 얼굴 표정 생성 기법)

  • Jung Sung-Uk;Kim Do-Yoon;Chung Myung-Jin;Kim Do-Hyoung
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.3
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    • pp.255-263
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    • 2006
  • In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

Smartphone Addiction Detection Based Emotion Detection Result Using Random Forest (랜덤 포레스트를 이용한 감정인식 결과를 바탕으로 스마트폰 중독군 검출)

  • Lee, Jin-Kyu;Kang, Hyeon-Woo;Kang, Hang-Bong
    • Journal of IKEEE
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    • v.19 no.2
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    • pp.237-243
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    • 2015
  • Recently, eight out of ten people have smartphone in Korea. Also, many applications of smartphone have increased. So, smartphone addiction has become a social issue. Especially, many people in smartphone addiction can't control themselves. Sometimes they don't realize that they are smartphone addiction. Many studies, mostly surveys, have been conducted to diagnose smartphone addiction, e.g. S-measure. In this paper, we suggest how to detect smartphone addiction based on ECG and Eye Gaze. We measure the signals of ECG from the Shimmer and the signals of Eye Gaze from the smart eye when the subjects see the emotional video. In addition, we extract features from the S-transform of ECG. Using Eye Gaze signals(pupil diameter, Gaze distance, Eye blinking), we extract 12 features. The classifier is trained using Random Forest. The classifiers detect the smartphone addiction using the ECG and Eye Gaze signals. We compared the detection results with S-measure results that surveyed before test. It showed 87.89% accuracy in ECG and 60.25% accuracy in Eye Gaze.

Inference of Korean Public Sentiment from Online News (온라인 뉴스에 대한 한국 대중의 감정 예측)

  • Matteson, Andrew Stuart;Choi, Soon-Young;Lim, Heui-Seok
    • Journal of the Korea Convergence Society
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    • v.9 no.7
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    • pp.25-31
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    • 2018
  • Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

Difference between Children's Self-Reports on Depression and Parents' Assessment of Children's Behaviors (아동의 우울보고에 따른 부모 아동행동평가의 차이)

  • Yang, Jae-Woong;Kim, Yu-Jin;Kim, Hyun-Soo;Shin, Kyung-Min;Shin, Yun-Mi
    • Journal of the Korean Academy of Child and Adolescent Psychiatry
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    • v.23 no.2
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    • pp.76-81
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    • 2012
  • Objectives : Childhood is a critical period involving various developmental tasks that need to be accomplished. Childhood depression has overall negative implications for certain areas of development, including cognition, emotion, social skills, academic achievement, and ability to cope with stress. Yet, because depression can be "masked" by accompanying behavioral problems, early detection and diagnosis of childhood depression is somewhat challenging. In this study, using the Korean version of the Child Behavior Checklist (K-CBCL), we evaluated the association between children's self reports on depression and parents' assessment of children's behaviors. Methods : Subjects were recruited from the S city, a cohort comprising a non-random convenience sample of 226, 10-year-old ethnic Koreans in their fourth year of elementary school and their parents. All participants underwent several tests, including Children's Depression Inventory (CDI) and K-CBCL. Results : A total of 226 children, including 166 boys (73.5%) and 60 girls (26.5%), participated in the study. The average CDI for the participants was 14.57 (SD=7.54). Two items on the K-CBCL, total scale of adjustment scale and social withdrawal problems, showed a close association with the CDI. Conclusion : Although much remains to be elucidated, after controlling for covariates, the results of this study suggested that behavioral problems observed in children may be closely associated with depression.