• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.28-41
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• 2020

This study selects two linear parks representing each culture and reveals the differences between them using a visitor survey as small data and social media analytics as big data based on the three components of the model of landscape perception. The 606 in Chicago, U.S., and the Gyeongchun Line in Seoul, Korea, are representative parks built on railroads. A total of 505 surveys were collected from these parks. The responses were analyzed using descriptive statistics, principal component analysis, and linear regression. Also, more than 20,000 tweets which mentioned two linear parks respectively were collected. By using those tweets, the authors conducted the clustering analysis and draw the bigram network diagram for identifying and comparing the placeness of each park. The result suggests that more diverse design concept links to less diversity in behavior; that half of the park users use the park as a shortcut; and that same physical exercise provides different benefits depending on the park. Social media analysis showed the 606 is more closely related to the neighborhoods rather than the Gyeongchun Line Forest. The Gyeongchun Line Forest was a more event-related place than the 606.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.361-363
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• 2017

최근 소셜 미디어의 이용률이 폭발적으로 증가함에 따라, 방대한 데이터가 네트워크로 쏟아져 나오고 있다. 이들 데이터는 기존의 정형 데이터뿐만 아니라 이미지, 동영상 등의 비정형 데이터가 있으며, 이들을 포괄하여 빅데이터라고 불린다. 이러한 빅데이터는 오피니언 마이닝, 테스트 마이닝 등의 기술적인 분석 기법과 빅데이터 요약 및 효과적인 표현방법에 대한 시각화 기법에 대하여 활발한 연구가 이루어지고 있다. 이 논문은 인기 있는 사회연결망 서비스인 Twitter의 트윗을 수집하고, 빅데이터 분석 기법인 텍스트 마이닝을 활용하여 2017년 대선에 대하여 분석하였다. 또한 분석된 자료의 효과적인 전달을 위해 워드 클라우드 진행하였다. 이 논문을 위하여 인기 있는 SNS인 Twitter의 최근 7일간 트윗(tweet)을 수집하고 분석하였다.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.44-56
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• 2023

This research explores the enhancement of park operation and management by analyzing the changing demands of park users. While traditional methods depended on surveys, there has been a recent shift towards utilizing social media data to understand park usage trends. Notably, most research has focused on text data from social media, overlooking the valuable insights from image data. Addressing this gap, our study introduces a novel method of assessing park usage using social media image data and then applies it to actual city park evaluations. A unique image analysis tool, built on Visual Question Answering (VQA) deep learning technology, was developed. This tool revealed specific city park details such as user demographics, behaviors, and locations. Our findings highlight three main points: (1) The VQA-based image analysis tool's validity was proven by matching its results with traditional text analysis outcomes. (2) VQA deep learning technology offers insights like gender, age, and usage time, which aren't accessible from text analysis alone. (3) Using VQA, we derived operational and management strategies for city parks. In conclusion, our VQA-based method offers significant methodological advancements for future park usage studies.

• 한국체육학회지인문사회과학편
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• v.56 no.4
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• pp.397-413
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• 2017

The purpose of this study is to introduce the case and to provide related information for the physical education major to handle and utilize the social big data through the exploratory study for the application of sports industry in the fourth industrial revolution. For this study, data was collected from the article database, which covers the keyword such as 'Social Big Data', 'Sports' and so on. The analyzed articles were 86 articles. As a results, The research on social big data applied to sports industry are as follows: 1) Analysis of major issues related to sports fans' interests and sports events, 2) A study on media sports engagement, 3) The prediction analysis of sports game based on the sentiment analysis, 4) Development of salary estimation model for professional player in sports, 5) Research trend analysis and so on. In conclusion, the social big data analysis technology in the sports industry and management can be utilized variously. Therefore, the specialists of the sports industry and management field need to learn the techniques, to acquire the know-how for the research project, to convert the convergence thinking.

• Knowledge Management Research
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• v.24 no.4
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• pp.195-218
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• 2023

This study empirically analyzes the effect of a sports club's performance and social media operations on online information search volume, reflecting fan engagement. Additionally, it confirms that such effect can vary depending on the issuance of sports fan tokens. The analysis of the data resulted in the support of all four hypotheses presented in this study. The team's goal differentials during the games exhibited a significant and positive effect on the online information search volume by fans. Furthermore, the quantity of a team's social media posts also showed a significant and positive effect on the online information search volume. The aforementioned effects of the team's game-related performance and social media activity on the online information search volume appeared to be strengthened when the sports fan tokens of the team were issued. This study conducts an empirical analysis of fan engagement in sports clubs and delves into the marketing dimensions of sports fan tokens. By doing so, it broadens the research scope within sports marketing and offers practical insights for the development of marketing strategies by sports clubs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.169-175
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• 2023

This paper analyzes museum-related big data using museums and gamification using social media big data, identifies and compares the perceptions of visitors mentioned in social media, and presents ways to use gamification. Based on the collected data, this paper aims to provide data by comparing and analyzing the perception of visitors to the museum and visitors to the museum using gamification. This paper investigates the perception of visitors through social media analysis using TEXTOM, a social media analysis tool, to identify differences in perception. As a result of the analysis, it was found that compared to museums that were previously viewed in the form of exhibitions, they felt fun and interest in visiting museums using geikipication. In addition, based on the analysis results of keywords and related keywords, the perception, motivation, and type of viewing of the museum of the National Museum of Korea and the Independence Hall of Korea were confirmed. In addition, it can be seen that the sense of achievement of visitors who visited the museum using gamification is higher than that of the existing museum. It is believed that by developing and activating game-related content in future museum visits, many visitors will be able to increase their interest in the museum and feel fun and interested. The results of the study are believed to be meaningful as basic data to grasp the overall perception of visitors to the museum, and based on this, it is expected that visitors will be able to see and experience the museum in various ways.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.5-6
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• 2019

빅데이터 분석을 위해 활용되는 데이터로는 뉴스, 블로그, SNS, 논문, 특허 그리고 센서로부터 수집된 데이터 등 매우 다양한 유형의 데이터가 있다. 특히, 신뢰성 있는 데이터를 실시간 제공하는 웹 데이터의 활용이 점차 확산되고 있다. 그리고 빅데이터의 활용이 다양한 분야로 점차 확산되고 웹 데이터가 매년 기하급수적으로 증가하면서, 최근 웹 데이터는 재난대응 미디어로써 매우 중요한 역할을 하고 있다. 또한, 빅데이터 분석에 활용되는 원천 데이터는 네트워크 형태이며, 최근 소셜 네트워크 분석을 통한 효과적인 상품 광고, 핵심 유전자 발굴, 신약 재창출 등 다양한 영역에서 네트워크 분석 기술이 사회와 인류에게 가치 있는 정보를 제공할 수 있는 가능성을 제시하면서 네트워크 분석 기술의 중요성이 부각되고 있다. 본 논문에서는 웹에서 제공하는 뉴스와 SNS 데이터를 이용해 수난 발생 및 규모 예측을 지원하는 웹 크롤러 및 네트워크 분석기술을 제안한다.

• Knowledge Management Research
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• v.21 no.3
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• pp.141-160
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• 2020

This study provides a new perspective on the effect of social media marketing activities (SMMA) on purchase intention in Korean cosmetic firms. The increasing use of social media has changed how firms engage their brand with consumers. This phenomenon triggered a need for this research to examine further the influence of SMMA on social brand engagement (SBE), brand equity (BE), and purchase intention (PI). The purpose of this paper is to investigate the effect of SMMA on purchase intention in Korean cosmetic firms with brand equity and social brand engagement. The factors of SMMA were identified based on previous literature reviews that have an impact on social media marketing activity. To empirically test the effects of SMMA, this study conducted a questionnaire survey on 219 social media users for data analysis out of the initial 332 survey data. The results reveal that all five SMMA elements are positively related to BE, SBE, and PI. The study enables cosmetic brands to forecast the future purchasing behavior of their customers more accurately and brings clarity to manage their assets and marketing activities as well.

• Smart Media Journal
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• v.9 no.1
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• pp.51-59
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• 2020

From PC communication to the development of the internet, a new term has been coined on the social media, and the social media culture has been formed due to the spread of smart phones, and the newly coined word is becoming a culture. With the advent of social networking sites and smart phones serving as a bridge, the number of data has increased in real time. The use of new words can have many advantages, including the use of short sentences to solve the problems of various letter-limited messengers and reduce data. However, new words do not have a dictionary meaning and there are limitations and degradation of algorithms such as data mining. Therefore, in this paper, the opinion of the document is confirmed by collecting data through web crawling and extracting new words contained within the text data and establishing an emotional classification. The progress of the experiment is divided into three categories. First, a word collected by collecting a new word on the social media is subjected to learned of affirmative and negative. Next, to derive and verify emotional values using standard documents, TF-IDF is used to score noun sensibilities to enter the emotional values of the data. As with the new words, the classified emotional values are applied to verify that the emotions are classified in standard language documents. Finally, a combination of the newly coined words and standard emotional values is used to perform a comparative analysis of the technology of the instrument.