• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.203-230
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• 2010

This study analyzed the development and growth of folksonomy by examining tags associated with 40 bestsellers on LibraryThing.com in 6-month intervals. It was found that tag values do not decrease but grow in terms of quantity and quality. Accordingly, we examined the major significances of the tags and their potential utilization as an expression of subjects. Our findings were as follows. First, the motivations for tagging can be categorized into personal information for search purposes, self-fulfillment such as sense of achievement, display of emotion and sharing of one's experience with others, or an altruistic objective that emphasizes sociality with a desire that one's actions might provide social benefits. According to our analysis, 74.12% of tags had a social motivation. Second, the total number of tags and the frequency of usage increased with time. Third, the categories that showed a high increase in tag usage were dates of publication and reading, key words, main characters, and book reviews. Tags related to subjects had the highest ratio. Fourth, among Library of Congress Subject Headings (LCSH), multiple genres, key words and main characters were assigned to books, and specific key words and other properties were added as time progressed. There was also a slight increase in the number of tags consistent with LCSH. Fifth, we found that key tags could serve as a compilation of terms that reflects the knowledge base of the corresponding era. Thus, folksonomy should be continuously monitored for its quantitative and qualitative development of the tags to make improvements on its formative disadvantages, and identify internal semantic significance, be actively utilized in conjunction with taxonomy as a flexible compilation of terms that incorporate the history of a specific era.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.1-21
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• 2016

IT-based subjective well-being (SWB) services, a main part of wellness IT, should measure the SWB state of individuals in an unrestrained, cost-effective manner. The dictionaries for sentiment analysis available in the market may be useful for this purpose, but obtaining proper sentiment values using only words from the sentiment lexicon is impossible; therefore, a new dictionary including wellness vocabulary is needed. The existing sentiment dictionaries link only a single sentiment value to a single sentiment word, although sentiment values may vary depending on personal traits. In this study, we develop an extended version of the SenticNet sentiment dictionary dubbed WellnessWordNet. SenticNet is considered the best and most expressive among the already existing sentiment dictionaries. Using the information provided by SenticNet, we created a database including the wellness states (estimated values) of stress, depression, and anger to develop the WellnessWordNet system. The accuracy of the system was validated through actual tests with live subjects. This study is unique and unprecedented in that i) an extended sentiment dictionary, WellnessWordNet, is developed; ii) values for wellness state language are offered; and iii) different sentiment values, namely contextual polarity, for people of the same gender or age group are suggested.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.291-303
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• 2021

In the modern society, the number of people in disaster vulnerable groups is rapidly increasing such as the elderly, the disabled, foreigners, and children. The common characteristics of the groups vulnerable to disasters are that they live in residence types that are exposed to disasters because they are impoverished and if they are exposed to disasters, recovery is a slow process. The purpose of this study is to identify the new risk issues by performing risk issue analysis on the targets of disaster vulnerable group and provide base data for the development of the policies. For the research method, this study centered on the cases of children and pregnant women out of the disaster vulnerable groups and focused on the issue data of social media throughout the past 10 years ('10~'19) and performed social network analysis. As a result, first, the development of the issue showed relevance in the occurrence of specific cases. Second, the awareness about the types, targets, and management method of crisis management was analyzed. Third, an analysis was performed on the sentiment words that considered the solution measures of risk issues or the characteristics of the targets and it was analyzed that there were word that triggered negative emotions. Therefore, it is anticipated for the base data to be used for the government and also for the local government to build an effective crisis management system of the rapidly changing disaster environment on the basis of the sentiment analysis performed on the people of the nation as well as public awareness.

• 한국노년학
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• v.41 no.4
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• pp.587-609
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• 2021

The purpose of this study was to investigate parenting experiences among grandmothers raising their grandchildren from grandmothers' perspective, and a variety of their physical health, psychological and social challenges they were facing in everyday life. In addition, this study explored new issues, changes, and difficulties grandparents and their grandchildren were going through during the COVID-19 pandemic. Seven grandmothers raising their grandchildren without their cohabiting parents participated in an in-depth interview, and the qualitative date were obtained using semi-structured questionnaires. Analyses identified 5 main categories: 1) my emotion, worries, and coping with parenting grandchildren, 2) difficulties and obstacles facing in real life of the parenting, 3) conflicts and coping with growing grandchildren who showed new characters, 4) relationships and emotions among grandparents, parents, and grandchildren, and 5) needs and desires toward social services and support. Sixteen themes and 60 sub-themes were also derived. The majority of grandmothers expressed diverse difficulties in their dail y lives including ambivalent emotions regarding grandchild-rearing(rewards and burden), economic hardships, physical health limitations, and a lack of communications with their grandchildren. Further, findings indicated profound generation conflicts which had been even deepened during school close period in COVID-19 pandemic and had been associated with increased hours of using internet and playing computer games. The top priority of the social service needs among interviewed grandmothers was learning support for their grandchildren. Emotional support and social support to cover their lack of family interactions, and financial support were the next of their desired social services. Implications to improve social services for grandparent-headed families are discussed.

• The Journal of the Convergence on Culture Technology
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• v.3 no.4
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• pp.101-106
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• 2017

This paper investigates how news media frame news editorials to deliver their subjective ideological stance through news discourse related with two candidates in 2016 U.S presidential election. For this purpose, 13 editorials were chosen and analyzed which appeared on the New York Time for the period from Sept. 1 to Sept. 30, almost two months prior to the election, giving special attention to the headlines of those editorials and the expressive linguistic forms in the selected two articles, based on the two theoretical frameworks-van Dijk' (1996)'s ideological square and Martin and White (2005)'s Appraisal Theory. The results are as follows: (1) editorials clearly supported Hillary Clinton; (2) following the appraisal theory, the category of 'feeling' was applied in expressing the preference for Hillary, whereas the strategy of judgment for Trump, where the strategy of 'emphasis' from the ideological framework were used for both candidates.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.151-157
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• 2005

There are successful multinationals like McDonald's, and International hotel chain. The reason is efficiency managing diversity workforces. Therefore, purpose of this study suggests practical guidelines to handling global workforce for creative ideas, diversity for network, and pool for superiority workforces. 1. The company or university we provided by training program for cross-culture seminar, and education program for global culture & manner. 2 The employees express their perceptions and feelings in their own language, the discussions were videotaped, and used for decreasing misfactors such as misperceptions, misevaluations, and mistrust. 3. It builds up various program for understanding cultural difference like seminar, world business manner, and costume & food culture for each country. 4. Top manager should keep in mind that cross-culture has diversity and consistency at the same time.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.362-369
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• 2006

3 차원 그래픽스에 적용하는 한국어 립씽크 합성 체계를 연구하여, 말소리에 대응하는 자연스러운 립씽크를 자동적으로 생성하도록 하는 디자인 시스템을 연구 개발하였다. 페이셜애니메이션은 크게 나누어 감정 표현, 즉 표정의 애니메이션과 대화 시 입술 모양의 변화를 중심으로 하는 대화 애니메이션 부분으로 구분할 수 있다. 표정 애니메이션의 경우 약간의 문화적 차이를 제외한다면 거의 세계 공통의 보편적인 요소들로 이루어지는 반면 대화 애니메이션의 경우는 언어에 따른 차이를 고려해야 한다. 이와 같은 문제로 인해 영어권 및 일본어 권에서 제안되는 음성에 따른 립싱크 합성방법을 한국어에 그대로 적용하면 청각 정보와 시각 정보의 부조화로 인해 지각의 왜곡을 일으킬 수 있다. 본 연구에서는 이와 같은 문제점을 해결하기 위해 표기된 텍스트를 한국어 발음열로 변환, HMM 알고리듬을 이용한 입력 음성의 시분할, 한국어 음소에 따른 얼굴특징점의 3 차원 움직임을 정의하는 과정을 거쳐 텍스트와 음성를 통해 3 차원 대화 애니메이션을 생성하는 한국어 립싱크합성 시스템을 개발 실제 캐릭터 디자인과정에 적용하도록 하였다. 또한 본 연구는 즉시 적용이 가능한 3 차원 캐릭터 애니메이션뿐만 아니라 아바타를 활용한 동적 인터페이스의 요소기술로서 사용될 수 있는 선행연구이기도 하다. 즉 3 차원 그래픽스 기술을 활용하는 영상디자인 분야와 HCI 에 적용할 수 있는 양면적 특성을 지니고 있다. 휴먼 커뮤니케이션은 언어적 대화 커뮤니케이션과 시각적 표정 커뮤니케이션으로 이루어진다. 즉 페이셜애니메이션의 적용은 보다 인간적인 휴먼 커뮤니케이션의 양상을 지니고 있다. 결국 인간적인 상호작용성이 강조되고, 보다 편한 인간적 대화 방식의 휴먼 인터페이스로 그 미래적 양상이 변화할 것으로 예측되는 아바타를 활용한 인터페이스 디자인과 가상현실 분야에 보다 폭넓게 활용될 수 있다.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.903-913
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• 2013

Social network services generate a considerable amount of social data every day on personal feelings or thoughts. This social data provides changing patterns of information production and consumption but are also a tool that reflects social phenomenon. We analyze negative emotional words from daily blogs to detect the change in blooger sentiment using multivariate control charts. We used the all the blogs produced between 1 January 2008 and 31 December 2009. Hotelling's T-square control chart control chart is commonly used to monitor multivariate quality characteristics; however, it assumes that quality characteristics follow multivariate normal distribution. The performance of a multivariate control chart is affected by this assumption; consequently, we introduce the support vector data description and its extension (K-control chart) suggested by Sun and Tsung (2003) and they are applied to detect the chage in blogger sentiment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.225-230
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• 2012

The opinion mining is that to use the existing data mining technology also uploaded blog to web, to use product comment, the opinion mining can extract the author's opinion therefore it not judge text's subject, only judge subject's emotion. In this paper, published opinion mining algorithms and the text using speech recognition API for non-voice data to judge the emotions suggested. The system is open and the Subject associated with Google Voice Recognition API sunwihwa algorithm, the algorithm determines the polarity through improved design, based on this interview, speech recognition, which implements the model.