• 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.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.107-117
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• 2011

The appearance of Web has brought an substantial revolution to all fields of society such knowledge management and business transaction as well as traditional information retrieval. In this paper, we propose an EFASIT(Extended Fuzzy AHP and SImilarity Technology) model considering the emotion analysis. And we combine the Extended Fuzzy AHP Method(EFAM) with SImilarity Technology(SIT) based on the domain corpus information in order to efficiently retrieve the document on the Web. The proposed the EFASIT model can generate the more definite rule according to integration of fuzzy knowledge of various decision-maker, and can give a help to decision-making, and confirms through the experiment.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.131-138
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• 2010

As ubiquitous computing environments evolve, various services are being provided as customer-centric services. In the past, studies based on personal profiles have been conducted to provide personalized services. However, identifying the user's preferences and supporting personalized services requires considerable data and time. To solve these problems, this paper proposes a system which provides the service by analyzing the user's emotions, rather than personalized service with personal profiles. In the proposed system, both speech analysis method and image analysis method are used to analyze the user's emotion. By using this emotion analysis method, we implemented the proposed system within the home network environment and finally provide effective personalized service.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• The Journal of Korean Association of Computer Education
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• v.11 no.2
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• pp.89-98
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• 2008

Providing appropriate interactions between learner and e- Learning system is an essential factor of a successful e-Learning system. Although many interaction functions are employed in multimedia Web-based Instruction content, learner experience a lack of similar feedbacks from educators in real- time due to the limitation of Human-Computer Interaction techniques. In this paper, an emotion recognition system via learner facial expressions has been developed and applied to a tutoring system. As human educators do, the system observes learners' emotions from facial expressions and provides any or all pertinent feedback. And various feedbacks can bring to motivations and get rid of isolation from e-Learning environments by oneself. The test results showed that this system may provide significant improvement in terms of interesting and educational achievement.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.687-692
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• 2016

The advent of Internet of Things(IoT) has increased the need for development of smart life based on Information and Communication Technology(ICT). By using IoT technology, we are able to control connected appliances using smart devices, such as smart phone. To support the smart life, there is a need to utilize emotion information for human behavior, based on both biorhythm and environment information. Research towards this goal suggests a smart lamp control system that has an effect on the human emotion. According to the PSI theory, the control system calculates the biorhythm with an algorithm that uses the human biorhythm, weather factors and walking amounts. The smart lamp works with the recommended color lights that can control the feelings and emotions of the user. Here, we will show the effect of physical and mental stability, health care, and accident prevention.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.475-488
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• 2001

With the increase of the number of Internet users, various methodologies have been proposed for the effective design of web page. However, the prior methodologies have focused only on the functional aspect of web page while ignoring the emotional aspects of web pages. This paper focuses on the emotional design of home pages and aims to provide a methodology to design a web page suitable for goal emotions. In order to achieve the main purpose, we have conducted three related studies. First, we have identified basic emotional dimensions representing various feeling users have from web pages as a pool of emotional adjectives. Second, we have identified key design elements related to the emotion by observing the design process of expert designers. Third, we examined the causal relation between the perceived emotion and designed elements. The results indicate that some design elements are more effective to produce certain feeling than others. This paper ends with limitations and implications of the study results.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.71-77
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• 2022

In this paper, a database is collected for extending the speech synthesis model to a model that synthesizes speech according to emotions and generating facial expressions. The database is divided into male and female data, and consists of emotional speech and facial expressions. Two professional actors of different genders speak sentences in Korean. Sentences are divided into four emotions: happiness, sadness, anger, and neutrality. Each actor plays about 3300 sentences per emotion. A total of 26468 sentences collected by filming this are not overlap and contain expression similar to the corresponding emotion. Since building a high-quality database is important for the performance of future research, the database is assessed on emotional category, intensity, and genuineness. In order to find out the accuracy according to the modality of data, the database is divided into audio-video data, audio data, and video data.