• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.123-131
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• 2019

Social network service users actively express and share their feelings about social issues and content of interest through postings. As a result, the sharing of emotions among individuals and community members in social network is spreading rapidly. Therefore, resulting in active research of emotion analysis on posting of users. However, There is insufficient research on emotion analysis for postings containing various emotions. In this paper, we propose a method that analyzes the emotions of an Instagram posts using hashtags and images. This method extracts representative emotion from user posts containing multiple emotions with 66.4% accuracy and 81.7% recall, which improves the emotion classification performance compared to the previous method.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.1-2
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• 2012

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

• Journal of the Korean Home Economics Association
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• v.50 no.3
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• pp.35-50
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• 2012

This study aims to investigate the effect of group home adolescents' emotion regulation and school adjustment. A survey was carried out on a total of 246 middle and high school students, who live in group homes. For data analysis, t-test, two-way ANOVA, Pearson's correlation analysis, multiple regression analysis, and reliability coefficients were carried out by using SPSS program(version 18.0). The findings of this study were as follows: First, there were significant differences in emotion regulation ability of group home adolescents, depending on the grade and gender. Second, there was an interaction by grade and gender in school adjustment of group home adolescents. Last, group home adolescents' gender, grade, and emotion regulation ability affected their school adjustment.

• Journal of the Korean Home Economics Association
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• v.49 no.10
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• pp.103-113
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• 2011

This study investigated the structural relationships between attachment, social competence, and emotion regulation. A survey was administered to 233 children of elementary school age(5th-6th grades) in G-city, Korea, using the IPPA-R, the Social Competence Inventory and an Emotion Regulation Scale. The collected data were then analyzed using a Canonical Correlation Analysis. First, the relationship between attachment and social competence was analyzed. The results showed that attachment and social competence have a positively correlated relationship. Peer attachments strongly affect the attributes of social competence when using a canonical variate analysis. Secondly, the relationship between attachment and emotional regulation was analyzed. The results showed that attachment and emotion regulation are also positively correlated. Maternal attachment particularly strongly affected the attributes of emotion regulation. Thirdly, the relationship between social competence and emotional regulation was analyzed. The results showed that social competence and emotional regulation have a positive relationship.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.5
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• pp.663-674
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• 2002

The objectives of this study were 1) to ascertain whether there was a difference in product performance (expressive or instrumental), which consumer recognized after using, related to consumer involvement toward clothing, 2) to clarify the effect of product performance on consumption emotion(positive or negative), 3) to investigate the effect of consumption emotion on satisfaction, and 4) to find out whether product performance had a direct effect on satisfaction toward product. The study was conducted in three steps. Through the two steps, measurement instruments were developed. At the last step, judgement sampling method were utilized to collect the data and subjects were 614 university students. Confirmatory factor analysis and structural equation model analysis were used to analyze the data. The results were as follows: 1) Consumer involvement had an effect on product performances but it was related to the expressive product performance more than to the instrumental product performance. 2) Product performance had positive influence on positive consumption emotion, while it had negative influence on negative consumption emotion. The results revealed that there were significant relationships between product performance and consumption emotion. 3) Positive consumption emotion had a positive effect on consumer satisfaction, on the other hand negative consumption emotion had a negative effect on consumer satisfaction. 4) Although the direct effects of product performances on satisfaction were larger than the indirect effects, product performance was greatly influential in consumption emotion and consumption emotion was strongly related to consumer satisfaction. Therefore, consumption emotion is an important determinant variable in the process of consumer satisfaction.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.1-9
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• 2018

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

• Asian Journal of Business Environment
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• v.5 no.4
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• pp.13-18
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• 2015

Purpose - Based on previous studies, this study extends current research and investigates whether the sub-factors of emotional intelligence increase job satisfaction or employee intrinsic and extrinsic motivation and perceived organizational support. Research design, data, and methodology - This study categorizes service employees' (consultants) emotional intelligence into four sub-factors: regulation of emotion, appraisal of emotion, utilization of emotion, and expression of emotion. The study then investigates the sub-factor effects on job satisfaction. A total of 353 valid questionnaires were collected. Results - The results of the path analysis showed that appraisal, utilization, and expression of emotion had a positive effect on intrinsic motivation, and utilization of emotion had a positive effect on extrinsic motivation. Extrinsic motivation had a positive effect on perceived organizational support and job satisfaction, and perceived organizational support had a positive effect on job satisfaction. Conclusion - As consultants' utilization of emotion is rendered as the ability to use emotion to improve performance, the conclusion is that such factors as monetary performance incentives are important in order to boost job satisfaction of the consultants.