• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.75-88
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• 2020

Given the development of information technology, various methods for efficient information delivery have been constructed as the method of delivering product information moves from offline and 2D to online and 3D. These attempts not only are about delivering product information in an online space where no real product exists but also play a crucial role in diversifying and revitalizing online shopping by providing virtual experiences to consumers. 360 product image is a photorealistic VR that allows a subject to be rotated and photographed to view objects in three dimensions. 360 product image has also attracted considerable attention considering that it can deliver richer information about an object compared with the existing still image photography. 360 product image is influenced by divergent production factors, and accordingly, a difference emerges in the responses of users. However, as the history of technology is short, related research is also insufficient. Therefore, this study aimed to grasp the responses of users, which vary depending on the type of products and the number of source images in the 360 product image process. To this end, a representative product among the product groups that can be frequently found in online shopping malls was selected to produce a 360 product image and experiment with 75 users. The emotional responses to the 360 product image were analyzed through an experimental questionnaire to which the semantic classification method was applied. The results of this study could be used as basic data to understand and grasp the sensitivity of consumers to 360 product image.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.539-554
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• 2018

Effective interaction between user and device is considered an important ability of IoT devices. For some applications, it is necessary to recognize human facial expressions in real time and make accurate judgments in order to respond to situations correctly. Therefore, many researches on facial image analysis have been preceded in order to construct a more accurate and faster recognition system. In this study, we constructed an automatic recognition system for facial expressions through two steps - a facial recognition step and a classification step. We compared various models with different sets of data with pixel information, landmark coordinates, Euclidean distances among landmark points, and arctangent angles. We found a fast and efficient prediction model with only 30 principal components of face landmark information. We applied several prediction models, that included linear discriminant analysis (LDA), random forests, support vector machine (SVM), and bagging; consequently, an SVM model gives the best result. The LDA model gives the second best prediction accuracy but it can fit and predict data faster than SVM and other methods. Finally, we compared our method to Microsoft Azure Emotion API and Convolution Neural Network (CNN). Our method gives a very competitive result.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.34-36
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• 2022

In the existing text sentiment analysis models, the entire text is usually directly modeled as a whole, and the hierarchical relationship between text contents is less considered. However, in the practice of sentiment analysis, many texts are mixed with multiple emotions. If the semantic modeling of the whole is directly performed, it may increase the difficulty of the sentiment analysis model to judge the sentiment, making the model difficult to apply to the classification of mixed-sentiment sentences. Therefore, this paper proposes a sentiment analysis model BHGCN that considers the text hierarchy. In this model, the output of hidden states of each layer of BERT is used as a node, and a directed connection is made between the upper and lower layers to construct a graph network with a semantic hierarchy. The model not only pays attention to layer-by-layer semantics, but also pays attention to hierarchical relationships. Suitable for handling mixed sentiment classification tasks. The comparative experimental results show that the BHGCN model exhibits obvious competitive advantages.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.415-420
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• 2013

In recent, many researches have been studying sensitivity and psychology of human on color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously. In this paper, we propose the method that extract the color and direction information in order to analyze the psychology in the picture of children. Histogram clustering is used for color information detection. Direction information extract from inner edge value. In the result of experiments, we shows that our method is similar to the pattern classification of the general method.

• Journal of The Association for Neo Medicine
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• v.1 no.2
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• pp.39-55
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• 1996

The theory of seven emotions is a unique theory in oriental medicine which describes the mutual relationship between body and mind of human. Although, the term 'Seven emotions' was not clearly indicated in ${\ulcorner}$The Yellow Emperor's Internal Classic(黃帝內經)${\lrcorner}$, it is appeared in ${\ulcorner}$A Treatise on the Three Catagories of Cause of Diseases(三因方)${\lrcorner}$ written by Chen Yan(陳言) in South-Song Dynasty. It seemed that Chen Yan explained seven emotions as the internal etiologic factor according to the classification of seven emotions of ${\ulcorner}$Ye-Gi(禮記)${\lrcorner}$ under the academic influence during Song Dynasy which emphasized more on the standard of right and wrong rather than individual emotion. Meditation or consideration modulates the function of spleen and stomach and the metabolism of blood and body fluid and it also controls the various emotions and maintains the equilibrium of human body. Human emotions are influenced by the changes of nature and deeply related to time and space including social-environmental factors. The function and strength of seven emotions: joy, anger, anxiety, worry, grief, apprehension and fright are determined by the external stimulation as the causes of illness.

• Journal of Oriental Neuropsychiatry
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• v.23 no.3
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• pp.33-46
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• 2012

Objectives : Emotion is composed by several basic feelings. This basic feeling is called Chiljung in Oriental Medicine. This study examines the positive and negative affects related to Chiljung. Methods : A total of 199 students of Dongshin university oriental medicine were tested by Questionnaire for Sasang Constitution ClassificationII(QSCCII) and Positive Affect and Negative Affect Schedule(PANAS). This study is used 156 students' data, excluding 43 students' data. Of the enrolled 156 students, four groups were classified by QSCCII. The positive and negative properties of Chiljung were determined by PANAS. These data were analyzed by frequency, Pearson's chi-square test, Crosstabulation Analysis with SPSS windows 15.0. Results : 1. Joy(喜) and Anger(怒) has directly-opposed emotional properties. 2. Thought(思) difficult to tell the difference between positive and negative, but it is distinct from Anxiety(憂) and Sorrow(悲) 3. Anxiety(憂) and Sorrow(悲) are superior in negative emotional properties. 4. Fear(恐) and Fright(驚) are superior in negative emotional properties, and Fright(驚) is superior over Fear(恐) in positive emotional properties. Conclusions : This study may serve as the foundation in identifying the psychological traits of Chiljung.