• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.47-64
• /
• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.331-332
• /
• 2022

현대사회에서는 다양한 방법, 통로로 자신들의 의견을 표현하고 또한 감정들을 표출한다. 이렇게 표출된 다양한 문장 및 감정들을 통해 각 연령별로 어떤 문제를 가지고 있는지, 무슨 상황에 놓여있는지 등을 알 수 있다. 본 논문에서는 이렇게 모여진 감성대화 말뭉치를 이용해 청소년들이 문장에서 추출한 단어들과 감정, 상황과 어떠한 연관성을 보이는지 확인해보고자 연구를 진행하였다. 청소년들이 남성의 경우 학교폭력 및 따돌림과 관련한 문제, 여성의 경우 가족관계와 관련한 문제와 연관성이 크다는 것을 확인하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.11
• /
• pp.133-140
• /
• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.5
• /
• pp.23-28
• /
• 2022

Today, the online market has grown rapidly due to the development of digital platforms and the pandemic situation. Therefore, unlike the existing offline market, the distinctiveness of the online market has prompted users to check online reviews. It has been established that reviews play a significant part in influencing the user's purchase intention through precedents of several studies. However, the current review writing method makes it difficult for other users to understand the writer's emotions by expressing them through elements like tone and words. If the writer also wanted to emphasize something, it was very cumbersome to thicken the parts or change the colors to reflect their emotions. Therefore, in this paper, we propose a technique to check the user's emotions through facial expression recognition using a camera, to automatically set colors for each emotion using research on existing emotions and colors, and give colors based on the user's intention.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2017.06a
• /
• pp.26-28
• /
• 2017

스마트폰 보유율과 모바일 이용 행태가 급변함에 따라 방송사에서는 양방향 서비스를 포함한 다양한 방송 서비스를 제공하려고 노력하고 있다. 양방향 서비스 방송에서 시청자가 보낸 문구를 실제 화면에 보여주기까지 PD 와 담당자들의 수작업이 필요하다. 하지만 하루 평균 약 7,200 건 (MBC 오늘아침 소통중계)의 양방향 서비스 관련 로그가 남게 되어, PD 가 일일이 판별하기에는 많은 노력이 따른다. 이러한 불필요한 노력을 줄이기 위해 본 논문에서는 감정 분석을 이용한 딥러닝 인공지능 기반 양방향 서비스 방송 소프트웨어 시스템을 제안한다. 첫째, 시청자들이 전송한 의견, 건의사항, 내용 등을 전처리 과정을 진행한다. 둘째, 감정 사전을 이용해 전처리 된 단어와 비교하여 시청자가 보낸 문구의 감정 점수를 계산한다. 셋째, 과거 실제 방송에 송출된 시청자 문구를 감정 점수와 함께 딥러닝을 이용하여 훈련시킨다. 본 논문의 성능을 평가하기 위해, 2017 년 생방송 오늘아침 소통중계에 사례연구를 진행하였고 효율성을 보였다. 앞으로 이러한 양방향 서비스 방송 소프트웨어 시스템 도입으로, PD 가 방송 제작에 더욱 집중 할 수 있도록 차별화된 방송을 준비하는데 크게 기여할 것이라 기대한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.655-660
• /
• 2005

This paper studied the feature parameters less affected by the emotional variation for the development of the robust speech recognition technologies. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. In this study, LPC cepstral coefficient, met-cepstral coefficient, root-cepstral coefficient, PLP coefficient, RASTA met-cepstral coefficient were used as a feature parameters. And CMS and SBR method were used as a signal bias removal techniques. Experimental results showed that the HMM based speaker independent word recognizer using RASTA met-cepstral coefficient :md its derivatives and CMS as a signal bias removal showed the best performance of $7.05\%$ word error rate. This corresponds to about a $52\%$ word error reduction as compare to the performance of baseline system using met - cepstral coefficient.

• Journal of Korea Multimedia Society
• /
• v.13 no.5
• /
• pp.802-816
• /
• 2010

To create gesture from a new text input is an important problem in computer games and virtual reality. Recently, there is increasing interest in gesture stylization to imitate the gestures of celebrities, such as announcer. However, no attempt has been made so far to stylize a gestures using emotion such as happiness and sadness. Previous researches have not focused on real-time algorithm. In this paper, we present a system to automatically make gesture animation from SMS text and stylize the gesture from emotion. A key feature of this system is a real-time algorithm to combine gestures with emotion. Because the system's platform is a mobile phone, we distribute much works on the server and client. Therefore, the system guarantees real-time performance of 15 or more frames per second. At first, we extract words to express feelings and its corresponding gesture from Disney video and model the gesture statistically. And then, we introduce the theory of Laban Movement Analysis to combine gesture and emotion. In order to evaluate our system, we analyze user survey responses.

• Journal of KIISE
• /
• v.42 no.4
• /
• pp.512-521
• /
• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.1037-1040
• /
• 2017

현재 머신러닝과 딥러닝의 기술이 빠른 속도로 발전하면서 수많은 인공지능 음성 비서가 출시되고 있지만, 발화자의 문장 내 존재하는 단어만 분석하여 결과를 반환할 뿐, 비언어적 요소는 인식할 수 없기 때문에 결과의 구조적인 한계가 존재한다. 따라서 본 연구에서는 인간의 의사소통 내 존재하는 비언어적 요소인 말의 빠르기, 성조의 변화 등을 수치 데이터로 변환한 후, "플루칙의 감정 쳇바퀴"를 기초로 지도학습 시키고, 이후 입력되는 음성 데이터를 사전 기계학습 된 데이터를 기초로 kNN 알고리즘을 이용하여 분석한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.1261-1264
• /
• 2021

현대 사회의 스트레스 문제가 심각해짐에 따라 각종 스트레스 관리 서비스가 꾸준히 개발되고 있으나, 해당 서비스들은 정서 상태 판단을 사용자가 직접 입력하는 데이터에만 의존하기 때문에 분석 결과를 완벽히 신뢰하기 어렵다. 본 연구에서 개발한 앱 S-detector는 스마트폰 사용 시간 및 빈도 정보를 자동으로 수집하고, 사용자가 작성한 일기 데이터에서는 감정 단어를 추출하여 스마트폰 사용 데이터와 일기 데이터를 각각 분석, 종합적으로 판단하는 알고리즘을 가지고 있다. 따라서 사용자가 심리·정신적 문제 가능성을 쉽게 인지하는 데 도움을 주는 앱으로서 해당 문제를 예방하거나 조기에 해결함을 목표로 한다.