• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1213-1216
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• 2017

Recently due to the increasing uncertainty of the disaster environment caused by climate change the effects of disasters have become larger due to the confluence and solidification diversification into disaster type and secondary damage. In this paper, we apply ICBMS through intelligent information technology and big data analysis to all processes of disaster safety management to minimize human, social, economic and environment damage from accidents or disasters, and prevention by control technology preparation by education and training expansion to remember by body, response by advanced technology of disaster response unmanned technology restoration by creation of local community environment ecosystem, investigation and analysis by intelligent information technology learn about disaster safety management 4.0. In addition, technical limitation and problems in the $4^{th}$ industrial revolution and the application of big data were analyzed and suggested alternatives and strategies to overcome.

• 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 Digital Convergence
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• v.15 no.2
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• pp.285-294
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• 2017

In this study, change of the view of love was analyzed by big data analysis in TV drama of married person's love. Two dramas were selected for analysis with opposite theme of love story. The sympathy of audience for the one month period from the end of the drama was analyzed by text mining and sentiment analysis. In particular, changes in the meaning of home meaning are identified. Home is not 'a place where a husband and wife play a social role', but 'a place where they can share real sympathy and one can be happy'. If individuals are not happy, they need to break their homes. In this study, the current divorce rate and the question regarding the matter should be considered. But based on Google Trends, in Korean society, interest in marriage were still higher than romance. It means that people prefer to 'a love to get marriage' in Korean modern society, than 'love for love affair'. It seems to be reflection of cognition change, marriage should be based on true love. This study is expected to be applied to the study of trend change through social media.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.97-104
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• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• Knowledge Management Research
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• v.22 no.2
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• pp.145-165
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• 2021

As of late December 2019, the spread of COVID-19 pandemic began which put the entire world in panic. In order to overcome the crisis and minimize any subsequent damage, the government as well as its affiliated institutions must maximize effects of pre-existing policy support and introduce a holistic response plan that can reflect this changing situation- which is why it is crucial to analyze social topics and people's interests. This study investigates people's major thoughts, attitudes and topics surrounding COVID-19 pandemic through the use of social media and big data. In order to collect public opinion, this study segmented time period according to government countermeasures. All data were collected through NAVER blog from 31 December 2019 to 12 December 2020. This research applied TF-IDF keyword extraction and LDA topic modeling as text-mining techniques. As a result, eight major issues related to COVID-19 have been derived, and based on these keywords, this research presented policy strategies. The significance of this study is that it provides a baseline data for Korean government authorities in providing appropriate countermeasures that can satisfy needs of people in the midst of COVID-19 pandemic.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.10-21
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• 2018

This study used text mining methodology to focus on the perceptions of the landscape embedded in text that users spontaneously uploaded to the "Taean Travel"blogpost. The study area is the Taean Coast National Park. Most of the places that are searched by 'Taean Travel' on the blog were located in the Taean Coast National Park. We conducted a network analysis on the top three places and extracted keywords related to the landscape. Finally, using a centrality and cohesion analysis, we derived landscape perceptions and the major characteristics of those landscapes. As a result of the study, it was possible to identify the main tourist places in Taean, the individual landscape experience, and the landscape perception in specific places. There were three different types of landscape characteristics: atmosphere-related keywords, which appeared in Kkotji Beach, symbolic image-related keywords appeared in Sinduri Coastal Sand Dune, and landscape objects-related appeared in Manlipo Beach. It can be inferred that the characteristics of these three places are perceived differently. Kkotji Beach is recognized as a place to appreciate a view the sunset and is a base for the Taean Coast National Park's trekking course. Sinduri Coastal Sand Dune is recognized as a place with unusual scenery, and is an ecologically valuable space. Finally, Manlipo Beach is adjacent to the Chunlipo Arboretum, which is often visited by tourists, and the beach itself is recognized as a place with an impressive appearance. Social media data is very useful because it can enable analysis of various types of contents that are not from an expert's point of view. In this study, we used social media data to analyze various aspects of how people perceive and enjoy landscapes by integrating various content, such as landscape objects, images, and activities. However, because social media data may be amplified or distorted by users' memories and perceptions, field surveys are needed to verify the results of this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.90-102
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• 2022

In recent years, as interest in healing increases, outdoor spaces with the concept of healing have been created. For more professional and in-depth planning and design, the perception and characteristics of outdoor healing places through social media posts were analyzed using NER. Text mining was conducted using 88,155 blog posts, and frequency analysis and clique cohesion analysis were conducted. Six elements were derived through a literature review, and two elements were added to analyze the perception and the characteristics of healing places. As a result, visitors considered place elements, date and time, social elements, and activity elements more important than personnel, psychological elements, plants and color, and form and shape when visiting healing places. The analysis allowed the derivation of perceptions and characteristics of healing places through keywords. From the results of the Clique, keywords, such as places, date and time, and relationship, were clustered, so it was possible to know where, when, what time, and with whom people were visiting places for healing. Through the study, the perception and characteristics of healing places were derived by analyzing large-scale data written by visitors. It was confirmed that specific elements could be used in planning and marketing.

• Fashion & Textile Research Journal
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• v.22 no.4
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• pp.481-494
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• 2020

This study examined consumer perceptions and consumer responses of Halal cosmetics and compared them with vegan cosmetics, which is a term similarly used. Twitter API of Python 3.7 was used to collect the keywords '#halalcosmetics' and '#vegancosmetics'. First, the main perception of consumers on Halal cosmetics focused on the original concept, image, expected efficacy, and factors to consider before purchase, religious keywords, labels and packaging for Halal cosmetics. Second, the main consumer perception of vegan cosmetics was the product concept, expected efficacy, factors to consider before purchase, related vegan industry, image, and vegan cosmetic components. Third, the consumer perceptions of Halal cosmetics and vegan cosmetics were similar in multiple ways, and both concepts included the Cruelty-free concept. Fourth, consumer satisfaction factors included cosmetics color, brand's consumer service, efficacy, smell, packaging design, reasonable price, effects, and formulation of cosmetics as well as satisfaction with Halal certification, and satisfaction of Vegan consumers. Consumer dissatisfaction factors included smell, flavor, delay in shipping, dissatisfaction with formulation, discrepancy between actual color and computer screen, concern and distrust about the use of prohibited ingredients for Halal products. This study examined consumer perceptions and reactions to Halal and vegan cosmetics to create basic knowledge for niche markets that are emerging as an ethical beauty consumption trend.

• Journal of the Korean Institute of Navigation
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• v.21 no.4
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• pp.103-116
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• 1997

This study was to assess Korean sailors' knowldege, attitudes and behaviors about AIDS. The subjects of this study were 379 safety-trainee sailors. Data were collected by self reporting on a questionnaire during February to March 1996. The results were as follows : The mean score on AIDS knowledge was 17.3 out of a possible maximum score of 24.0. With respect to diseas transmission , only 45.6-86.5percent of the sailors correctly indicated that causal contact does not lead to contraction AIDS. The younger, unmarried , and educated groups had a higher level of knowledge about AIDS. With respect t sailors' attitudes about ADIS, 85.2 percent of the sailors reported that the AIDS is as big a problem as the media suggested, and over half of the sailors(53.8%) reported that they are being afraid of getting AIDS. One attitude, which was most pervasive(903.1 percent agreeing) was that it is important for sailors to receive AIDS education as a part of social education classes. In attitudes , there was statistical significance by age group, marital statistical signifiacance by age group , marital status, and educational level. With respect to sailor's preventive behaviors about AIDS, the mean score was 7.1 out of a possible maximum score of 9.0. It was shown that the older age, married groups had a higher level of preventive behaviors about AIDS.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.149-156
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• 2022

In the current COVID-19 pandemic, fake news and misinformation related to COVID-19 have been causing serious confusion in our society. To accurately detect such fake news, social context-based methods have been widely studied in the literature. They detect fake news based on the social context that indicates how a news article is propagated over social media (e.g., Twitter). Most existing COVID-19 related datasets gathered for fake news detection, however, contain only the news content information, but not its social context information. In this case, the social context-based detection methods cannot be applied, which could be a big obstacle in the fake news detection research. To address this issue, in this work, we collect from Twitter the social context information based on CoAID, which is a COVID-19 news content dataset built for fake news detection, thereby building CoAID⁺ that includes both the news content information and its social context information. The CoAID⁺ dataset can be utilized in a variety of methods for social context-based fake news detection, thus would help revitalize the fake news detection research area. Finally, through a comprehensive analysis of the CoAID⁺ dataset in various perspectives, we present some interesting features capable of differentiating real and fake news.