• The Journal of the Korea Contents Association
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• 제20권11호
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• pp.110-118
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• 2020

Social media data serves as a proxy indicator to understand the problems and the future of public opinion in Korean society. This research used 109,015 news data from 2016 to 2018 to analyze the sensitivity of the elderly and employment in Korean society, and explored the possibility of expanding the labor force in Korean society, which is facing a cliff between the elderly and the population. Topic keywords for employment of the elderly include "elderly*employment", "elderly*employment", and "elderly*wage". As a result of the analysis, positive sensitivity prevails for most of the period, and it is possible to expand the working-age population. Positive feelings about expanding employment opportunities for the elderly and negative feelings about low wages have brought to light the reality of the elderly who are still poor despite their work. In this study, social big data was used to analyze the perceptions and sensibilities of Korean society related to the elderly and employment through hierarchical crowd analysis and related text mining analysis.

• Journal of Digital Contents Society
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• 제18권3호
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• pp.585-592
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• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• The Journal of Society for e-Business Studies
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• 제23권4호
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• KIPS Transactions on Software and Data Engineering
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• 제11권9호
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• pp.381-390
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• 2022

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word 'depress,' using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

• Journal of Industrial Convergence
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• 제22권3호
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• pp.13-25
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• 2024

This study explores research topics related to dance performances published in Korea based on big data and examines research trends that change according to the trend of the times. The results derived from topic modeling analysis are as follows. (1) Six major topics were derived: a study on marketing strategies and development plans for dance performances, (2) a study on the re-watching factors of dance performance space and performance satisfaction, (3) a study on the popularity and contribution of dance performances in the stage environment, (4) a study on the current status of dance performances and the convergence of dance group operations, (5) a study on the definition of dance performances using various social media, and (6) a study on the direction and development of technology-applied dance performance contents. Accordingly, research trends and topics related to dance, including dance performances, social changes, key keywords of researchers' change interests were extracted, and keywords were compared and analyzed to present academic changes and countermeasures. Accordingly, the need for research to apply new technologies was emphasized as it diversified and fused.

• Knowledge Management Research
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• 제22권2호
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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 Environmental Impact Assessment
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• 제31권5호
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• pp.310-320
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• 2022

This study aimed to identify the change in the public perception of "bats" after the outbreak of the coronavirus (COVID-19) infection. Text mining and network analysis were conducted for blog posts, the largest social network in Korea. We collected 9,241 Naver blog posts from 2019 to 2020 just before the outbreak of COVID-19 in Korea. The data were analyzed with Python and NetMiner 4.3.2, and the public's perception of bats was examined through the relationship of keywords by period. Findings indicated that the frequency of bat keywords in 2020 increased more than 25 times compared to 2019, and the centrality value increased more than three times. The perception of bats changed before and after the outbreak of the pandemic. Prior to COVID-19, bats were highly recognized as a species of wildlife while in the first half of 2020, they were strongly considered as a threat to human society in relation to infectious diseases and health. In the second half of 2020, it was confirmed that the area of interest in bats expanded as the proportion of ecological and cultural types ofresearch increased. This study seeks to contribute to the expansion and direction of future research in bats by understanding the public's interest in the potential impact of the species as disease hosts post the COVID-19 pandemic.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제18권6호
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• pp.662-672
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• 2017

Child sexual crimes have increased, but there has been no effective plan to combat this. Films reporting problems, amplify the attentions and propose countermeasures, which leads to changes. The current study examined the audiences' reactions to child sexual crime faction films using text-mining. The analysis of Naver's 2,727 blogs showed realistic words while 3,000 review comments' analysis demonstrated emotional responses. The positive and negative emotional category and degree were also different. In , the higher degree of negative emotions, such as 'angry' and 'unpleasant' appeared frequently. In , only negative emotional worlds were used. On the other hand, 'sad' was the highest ranked word, and the negative level was weak. In , 'good' a positive emotional word solely emerged. The audiences perceived the accidents objectively before release while they expressed their emotions and feelings after watching the movies. caused explosive anger and organized the participating citizens for changes. This movie provided an opportunity to enforce a legislative bill intensifying heavy punishments. The present study is significant in scrutinizing the audiences' diverse emotional reactions and discusses the future direction of society prosecution movies. Based on the text analysis of the audiences' linguistic expressions, a future study will be needed to hierarchically classify the diverse emotional expressions.

• Journal of the Korea Convergence Society
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• 제13권5호
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• pp.313-325
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• 2022

The purpose of this study is to examine the changes in the elderly healthcare app market through text mining analysis and to present basic data for activating elderly healthcare apps. Data collection was conducted on Naver, Daum, blog web, and cafe. As for the research method, text mining, TF-IDF(Term frequency-inverse document frequency), emotional analysis, and semantic network analysis were conducted using Textom and Ucinet6, which are big data analysis programs. As a result of this study, a total of six categories were finally derived: resolving the healthcare app information gap, convergence healthcare technology, diffusion media, elderly healthcare app industry, social background, and content. In conclusion, in order for elderly healthcare apps to be accepted and utilized by the elderly, they must have a good diffusion infrastructure, and the effectiveness of healthcare apps must be maximized through the active introduction of convergence technology and content development that can be easily used by the elderly.

• Journal of the Korean Institute of Landscape Architecture
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• 제46권5호
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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.