• 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.

• Anxiety and mood
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• v.1 no.1
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• pp.18-24
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• 2005

The Diagnostic and Statistical Manual 4th edition (DSM-IV) has been widely accepted and used for international classification of mental disorder. The DSM has been changed to improve diagnostic reliability and validity through descriptive and categorical approaches which was undertaken atheoretically. The authors reviewed current studies about the DSM-IV classification system and the diagnostic issues of representative categories of anxiety disorder. The authors concluded that the anxiety disorder classification system in DSM-IV has limitations such as a lack of empirical consideration for overlapping features of anxiety disorders and a lack of discriminant validity. To improve diagnostic validity and revise the current DSM-IV classification system, the authors suggested 1) more longitudinal studies for collecting empirical evidence, 2) decreasing the dependence upon operational criteria, 3) deceasing diagnostic boundary blurring, 4) developing disease specific biological diagnostic techniques and 5) continued collaboration between the DSM and International Classification of Diseases (ICD) systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.80-86
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• 2010

This paper presents automated music story video generation technique as one of entertainment features that is equipped in multimedia system of the vehicle. The automated music story video generation is a system that automatically creates stories to accompany musics with photos stored in user's mobile phone by connecting user's mobile phone with multimedia systems in vehicles. Users watch the generated music story video at the same time. while they hear the music according to mood. The performance of the automated music story video generation is measured by accuracies of music classification, photo classification, and text-keyword extraction, and results of user's MOS-test.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.134-138
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• 2006

Depression is the most common and widespread mood disorder. About 20% of the population might suffer a major, incapacitating episode of depression during their lifetime. This disorder can be classified into two types: major depressive disorders and bipolar disorder. Since pharmaceutical treatments are different according to types of depression disorders, correct and fast classification is quite critical for depression patients. Yet, classical statistical method, such as minnesota multiphasic personality inventory (MMPI), have some difficulties in applying to depression patients, because the patients suffer from concentration. We used electroencephalogram (EEG) analysis method fer classification of depression. We extracted nonlinearity of information flows between channels and estimated approximate entropy (ApEn) for the EEG at each channel. Using these attributes, we applied two types of data mining classification methods: decision tree and possibilistic support vector machines (PSVM). We found that decision tree showed 85.19% accuracy and PSVM exhibited 77.78% accuracy for classification of depression, 30 patients with major depressive disorder and 24 patients having bipolar disorder.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.19-24
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.

• ETRI Journal
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• v.44 no.3
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• pp.413-425
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• 2022

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification-based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa-based classification of Hindi poetry. For the task, we primarily used lexical features in a bag-of-words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec-based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.722-725
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• 2006

Studies on the ways to construct agreeable sound-amenity have been processed in Korea recently and Virtual Acoustics Field Simulation System (VAFSS) which is an active acoustics reproducing system has been made as a technique to realize the results of the study. This system catches the changes of surroundings and produce sounds which go well with the mood of the space. The fact that a man thinks a sound goes well with factors of the environment should be an individual evaluation. Thus, the standards to classify factors influencing the preference of the sound, which can be judged by the environment, are needed. This study suggests the standards of factors to provide agreeable sound for people according to changes of the time and other elements. Among the factors influencing environment, the temperature, the humidity and the wind were suggested as standards of discomfort Index and wind chin temperature. Besides, only the intensity of illumination has been chosen to estimate the intensity of radiation as a part of factors of the whether.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.67-68
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• 2019

머신러닝을 활용한 이미지 분류는 단순 사물을 넘어서 사람의 감성과 같은 추상적이고 주관적인 개념에도 적용되고 있다. 이 중에서도 합성곱 신경망을 통한 이미지의 감정 분류 연구가 더욱 활성화되고 있다. 그럼에도 다양한 멀티미디어들을 머신러닝 알고리즘으로 분석하고 이를 의미있는 결과로 재생성하기는 매우 복잡하고 까다롭다. 본 연구에서는 기존 연구를 개선시켜 음악 데이터를 다층퍼셉트론 모델을 통해 분류된 이미지와 결합한 동영상을 파이썬의 다양한 라이브러리를 통해 자동으로 생성하였다. 이를 통해 특정 분위기로 분류된 이미지들과 이에 어울리는 음악을 매칭시켜 유의미한 새로운 멀티미디어를 자동으로 생성할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.216-219
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• 2011

인터넷 기술이 빠르게 발전하고 변화하여 현재는 많은 수의 컨텐츠와 프로그램 채널이 IP 네트워크를 통해 제공되면서 컨텐츠 서비스 사업자들은 좀 더 향상된 추천시스템이 필요하게 되었다. 그리고 사용자 참여중심의 인터넷 환경인 Web 2.0 시대가 도래하면서 사용자가 직접 생성한 정보들을 활용하는 다양한 연구가 진행되고 있다. 본 논문에서는 타겟 아이템에 대해 인터넷 상에 수많은 사용자들이 생성한 정보들을 ConceptNet을 활용하여 감정벡터를 추출하고 장르별로 분류하는 방법을 결합한 새로운 형태의 영화 추천시스템을 제안한다. 공개용 영화 데이터인 MovieLens 데이터 셋을 이용하여 실험하였고 성능평가는 RMSE 방법과 다양한 추천평가방법으로 기존 협업 필터링 추천시스템과 비교하였으며 실험 결과 기존방식보다 향상된 성능을 보였다.