• Title/Summary/Keyword: Emotion analysis

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Analyzing and classifying emotional flow of story in emotion dimension space (정서 차원 공간에서 소설의 지배 정서 분석 및 분류)

  • Rhee, Shin-Young;Ham, Jun-Seok;Ko, Il-Ju
    • Korean Journal of Cognitive Science
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    • v.22 no.3
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    • pp.299-326
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    • 2011

  • The text such as stories, blogs, chat, message and reviews have the overall emotional flow. It can be classified to the text having similar emotional flow if we compare the similarity between texts, and it can be used such as recommendations and opinion collection. In this paper, we extract emotion terms from the text sequentially and analysis emotion terms in the pleasantness-unpleasantness and activation dimension in order to identify the emotional flow of the text. To analyze the 'dominant emotion' which is the overall emotional flow in the text, we add the time dimension as sequential flow of the text, and analyze the emotional flow in three dimensional space: pleasantness-unpleasantness, activation and time. Also, we suggested that a classification method to compute similarity of the emotional flow in the text using the Euclidean distance in three dimensional space. With the proposed method, we analyze the dominant emotion in korean modern short stories and classify them to similar dominant emotion.

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Acoustic parameters for induced emotion categorizing and dimensional approach (자연스러운 정서 반응의 범주 및 차원 분류에 적합한 음성 파라미터)

  • Park, Ji-Eun;Park, Jeong-Sik;Sohn, Jin-Hun
    • Science of Emotion and Sensibility
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    • v.16 no.1
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    • pp.117-124
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    • 2013

  • This study examined that how precisely MFCC, LPC, energy, and pitch related parameters of the speech data, which have been used mainly for voice recognition system could predict the vocal emotion categories as well as dimensions of vocal emotion. 110 college students participated in this experiment. For more realistic emotional response, we used well defined emotion-inducing stimuli. This study analyzed the relationship between the parameters of MFCC, LPC, energy, and pitch of the speech data and four emotional dimensions (valence, arousal, intensity, and potency). Because dimensional approach is more useful for realistic emotion classification. It results in the best vocal cue parameters for predicting each of dimensions by stepwise multiple regression analysis. Emotion categorizing accuracy analyzed by LDA is 62.7%, and four dimension regression models are statistically significant, p<.001. Consequently, this result showed the possibility that the parameters could also be applied to spontaneous vocal emotion recognition.

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Emotion Recognition Method Using Heart-Respiration Connectivity (심장과 호흡의 연결성을 이용한 감성인식 방법)

  • Lee, Dong Won;Park, Sangin;Whang, Mincheol
    • Science of Emotion and Sensibility
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    • v.20 no.3
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    • pp.61-70
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    • 2017

  • Physiological responses have been measured to recognize emotion. Although physiological responses have been interrelated between organs, their connectivities have been less considered for emotion recognizing. The connectivities have been assumed to enhance emotion recognition. Specially, autonomic nervous system is physiologically modulated by the interrelated functioning. Therefore, this study has been tried to analyze connectivities between heart and respiration and to find the significantly connected variables for emotion recognition. The eighteen subjects(10 male, age $24.72{\pm}2.47$) participated in the experiment. The participants were asked to listen to predetermined sound stimuli (arousal, relaxation, negative, positive) for evoking emotion. The bio-signals of heart and respiration were measured according to sound stimuli. HRV (heart rate variability) and BRV (breathing rate variability) spectrum were obtained from spectrum analysis of ECG (electrocardiogram) and RSP (respiration). The synchronization of HRV and BRV spectrum was analyzed according to each emotion. Statistical significance of relationship between them was tested by one-way ANOVA. There were significant relation of synchronization between HRV and BRV spectrum (synchronization of HF: F(3, 68) = 3.605, p = 0.018, ${\eta}^2_p=0.1372$, synchronization of LF: F(3, 68) = 5.075, p = 0.003, ${\eta}^2_p=0.1823$). HF difference of synchronization between ECG and RSP has been able to classify arousal from relaxation (p = 0.008, d = 1.4274) and LF's has negative from positive (p = 0.002, d = 1.7377). Therefore, it was confirmed that the heart and respiration to recognize the dimensional emotion by connectivity.

  • https://doi.org/10.14695/KJSOS.2017.20.3.61 인용 PDF KSCI

An Empirical Study on the Sub-factors of Middle School Character Education using Social Network Analysis (사회 네트워크 분석을 이용한 중등 인성 교육의 세부요인에 관한 실증 연구)

  • Kim, Hyojung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.13 no.2
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    • pp.87-98
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    • 2017

  • The advancements in scientific technology and information network in the 21st century allow us to easily acquire a desired knowledge. In the midst of today's informatization, globalization, and cultural diversification, adolescents experience emotional confusion while accommodating diverse cultures and information. This study aimed at examining three aspects of character suggested by the Ministry of Education, which are ethics, sociality, and emotion, and the actual sub-factors required for character education. To that end, a survey was conducted with adolescents who were at a character-building age, and social network analysis (SNA) was performed to determine the effect of character education on the sub-factors. The statistics program SPSS was used to investigate the general traits of the subjects and the validity of the research variables. The 2-mode data that were finally selected were converted to 2-mode data using NetMinder 4, which is a network analysis tool. Furthermore, a data network was established based on a quasi-network that represents the relationships between ethics, sociality, and emotion. The results of this study showed that the subjects considered honesty and justice to be the sub-domains of the ethics domain. In addition, they identified sympathy, communication, consideration for others, and cooperation as the sub-domains of the sociality domain. Finally, they believed that self-understanding and self-control were the sub-domains of the emotion domain.

  • https://doi.org/10.17662/ksdim.2017.13.2.087 인용 PDF KSCI