• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.985-987
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.249-255
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• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1123-1126
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• 2004

Recognizing emotion in speech is required lots of spoken language corpus not only at the different emotional statues, but also in individual languages. In this paper, we focused on the changes speech signals in different emotions. We compared the features of speech information like formant and pitch according to the 4 emotions (normal, happiness, sadness, anger). In Korean, pitch data on monophthongs changed in each emotion. Therefore we suggested the suitable analysis techniques using these features to recognize emotions in Korean.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.23-38
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• 2007

Since the players in online games seek fun, emotional aspects may be important factors in the pleasure-oriented information systems. This study attempted to verify the effects of characteristic factors of games (fun, challenge, compensation, and diversity) on emotion and customer satisfaction in game portal sites. The study established the research model on the foundation of precedent researches related to online games, emotion, and customer satisfaction. The study conducted online and offline survey on individuals with experiences in using online game portal sites. 206 samples were used to test the research hypotheses. By using Structural Equation Modeling, The study found the following results. First, it was revealed that characteristics of games had positive effects on emotion in game portal sites and indirect effects on user satisfaction. Second, the study analyzed the difference between low user group and high user group. It was revealed that the characteristics of games had positive effects on emotion in the low user group, but only challenge factor influenced on emotion in the high user group.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1068-1082
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• 2015

Due to the rapid advancement of the mobile technology, smart phones have been widely used in the current society. This lead to an easier way to retrieve video contents using web and mobile services. However, it is not a trivial problem to retrieve particular video contents based on users' specific preferences. The current movie recommendation system is based on the users' preference information. However, this system does not consider any emotional means or perspectives in each movie, which results in the dissatisfaction of user's emotional requirements. In order to address users' preferences and emotional requirements, this research proposes a movie recommendation technology to represent a movie's emotion and its associations. The proposed approach contains the development of emotion ontology by representing the relationship between the emotion and the concepts which cause emotional effects. Based on the current movie metadata ontology, this research also developed movie-emotion ontology based on the representation of the metadata related to the emotion. The proposed movie recommendation method recommends the movie by using movie-emotion ontology based on the emotion knowledge. Using this proposed approach, the user will be able to get the list of movies based on their preferences and emotional requirements.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2463-2466
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• 2003

This paper studied the pattern recognition algorithm and feature parameters for emotion recognition. In this paper, KNN algorithm was used as the pattern matching technique for comparison, and also VQ and GMM were used lot speaker and context independent recognition. The speech parameters used as the feature are pitch, energy, MFCC and their first and second derivatives. Experimental results showed that emotion recognizer using MFCC and their derivatives as a feature showed better performance than that using the Pitch and energy Parameters. For pattern recognition algorithm, GMM based emotion recognizer was superior to KNN and VQ based recognizer

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.501-510
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• 2010

The knowledge base on emotional information is one of the key elements in the implementation of emotion retrieval systems for contents design of Mobile devices. This study proposed a new approach to the knowledge base implementation by automatically extracting color components from full-color images. In this study, the validity of the proposed method was empirically tested. Database was developed using 100 interior images as visual stimuli and a total of 48 subjects participated in the experiment. In order to test the reliability of the proposed 'emotional information knowledge base', firstly 'recall ratio' that refers to frequencies of correct images from the retrieved images was derived. Secondly, correlation Analysis was performed to compare the ratings by the subjects to what the system calculated. Finally, the rating comparison was used to run a paired-sample t-test. The analysis demonstrated satisfactory recall ration of 62.1%. Also, a significant positive correlation (p<.01) was observed from all the emotion keywords. The paired Sample t-test found that all the emotion keywords except "casual" retrieved the images in the order from more relevant to less relevant images and the difference was statistically significant (t(9)=5.528, p<.05). Findings of this study support that the proposed 'emotional information knowledge base' established only with color information automatically extracted from images can be effectively used for such visual stimuli search tasks as commercial interior images.

• Asia Marketing Journal
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• v.14 no.4
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• pp.57-75
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• 2013

It is a well-established theory that emotion is influential in cognitive processing. Extensive prior research on emotion has shown that emotional factors, such as affect, mood, and feeling, play as information indicating whether he or she has enough knowledge. Most of their findings focused on the effect of emotional valence (i.g., one's subjective positivity or negativity related with the emotion). Recently, several studies on emotion suggest that there is another dimension of emotion, which affects the type of cognitive processing. The studies argue that emotional certainty facilitates heuristic processing, whereas emotional uncertainty promotes systematic processing. Based on the findings, current study examines the effect of certainty on attitudes and recall. Specifically, the authors investigate the effect of certainty on how much effort individuals use to process advertising information and how certainty affects attitude formation toward the advertised product. The authors also focus on recall to clarify the working mechanism of certainty on attitudes, because recall performance reflects the depth of information processing. Based on previous findings, the authors hypothesize that uncertainty (vs. certainty) leads to more favorable attitudes as well as better recall, and conduct an experiment using a fictitious advertisement with 218 participants. The results confirm the predicted effects of certainty only on attitudes not recall. A possible explanation of this discrepancy between attitudes and recall lies in the measurement method, unaided recall. To rule out this possibility, the authors perform an additional analysis with the participants who recall any correct information of the target advertisement. The results show certainty has a negative effect on both attitudes and recall. A bootstrapping test reveals that recall mediates the effect of certainty on attitudes. This result confirms that certainty decreases elaboration, which in turn leads to less favorable attitudes relative to uncertainty. Additionally, our data shows the association among certainty, recall, and attitudes by showing the indirect effect of certainty on attitudes via recall. This research encourages practitioners in the field to emphasize that they should focus on target audiences' emotional certainty before they provide the persuasive message, by showing that uncertainty promotes effortful processing, which in turn leads to better memory and more favorable attitudes.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.43-52
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• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.