• 스마트미디어저널
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• 제2권2호
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.391-406
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• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

• 패션비즈니스
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• 제27권5호
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• pp.78-92
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• 2023

Fashion trends are actively disseminated through social media, which influences both their propagation and consumption. This study explored how users perceive subversive basic fashion in social media videos, by examining the associated concepts and characteristics. In addition, the factors contributing to the style's social media dissemination were identified and its distinctive features were analyzed. Through text mining analysis, 80 keywords were selected for semantic network and CONCOR analysis. TF-IDF and N-gram results indicate that subversive basic fashion involves transformative design techniques such as cutting or layering garments, emphasizing the body with thin fabrics, and creating bold visual effects. Topic modeling suggests that this fashion forms a subculture that resists mainstream norms, seeking individuality by creatively transforming the existing garments. CONCOR analysis categorized the style into six groups: forward-thinking unconventional fashion, bold and unique style, creative reworking, item utilization and combination, pursuit of easy and convenient fashion, and contemporary sensibility. Consumer actions, linked to social media, were shown to involve easily transforming and pursuing personalized styles. Furthermore, creating new styles through the existing clothing is seen as an economic and creative activity that fosters network formation and interaction. This study is significant as it addresses language expression limitations and subjectivity issues in fashion image analysis, revealing factors contributing to content reproduction through user-perceived design concepts and social media-conveyed fashion characteristics.

• Genomics & Informatics
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• 제18권2호
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• pp.16.1-16.5
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• 2020

There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain.

• The Journal of Asian Finance, Economics and Business
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• 제6권3호
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• pp.173-183
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• 2019

This study aims to utilize big data contents of news and social media for developing a corporate strategy of multinational enterprises and their global decision-making through the data mining technique, especially text mining. In this paper, the data of 2 news media (BBC and CNN) and 2 social media (Facebook and Twitter) were collected for the three global leading Korean companies (Samsung, Hyundai Motor Company, and LG) from April, 2018 to April, 2019. The findings of this paper have shown that traditional news media and also modern social media have become devastating tools to extract global trends or phenomena for businesses. Moreover, this presents that a company can adopt a two-track strategy through two different types of media by deriving the key issues or trends from news media channels and also grasping consumers' sentiments, preference or issues of interest such as battery or design from social media. In addition, analyzing the texts of those media and understanding the association rules greatly contribute to the comparison between two different types of media channels to see the difference. Lastly, this provides meaningful and valuable data empowered insights to find a future direction comprehensively and develop a global strategy for sustainability of business.

• Asian Journal of Innovation and Policy
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• 제11권2호
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• pp.202-221
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• 2022

The media has depicted an abrupt socio-technological change in the Republic of Korea with the 4th Industrial Revolution. Because technologies cannot realize their potential without social acceptance, studying conflicts incurred by such a change is imperative. However, little literature has focused on conflicts caused by technologies. Therefore, the current study investigated media coverage regarding conflicts related to the 4th Industrial Revolution from 2016 to 2020 in the Republic of Korea, applying text-mining techniques. We found that the overall amount and coverage pattern conforms to the issue attention cycle. Also, the three major topics ("SMEs & Startups," "Mobility Conflict," and "Human & Technology") indicate quarrels between conflicting social entities. Moreover, the temporal change in media coverage implies the political use of the term rather than technological. However, we also found the media's deliberative discussion on the socio-technological impact. This study is significant because we expanded the discussion on media coverage of technologies to the realm of social conflicts. Furthermore, we explored the news articles of the recent five years with a text-mining approach that enhanced the objectivity of the research.

• Human Ecology Research
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• 제59권1호
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• pp.23-43
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• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• 경영정보학연구
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• 제21권2호
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• pp.117-137
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• 2019

빅데이터 시대를 맞아 다양한 도메인에서 수없이 많은 데이터들이 생산되면서 데이터 사이언스가 대중화 되었고, 데이터의 힘이 곧 경쟁력인 시대가 되었다. 특히 전 세계 데이터의 80% 이상을 차지하는 비정형 데이터에 대한 관심이 부각되고 있다. 소셜 미디어의 발전과 더불어 비정형 데이터의 대부분은 텍스트 데이터의 형태로 발생하고 있으며, 마케팅, 금융, 유통 등 다양한 분야에서 중요한 역할을 하고 있다. 하지만 이러한 소셜 미디어를 활용한 텍스트 마이닝은 수치형 데이터를 활용한 데이터 마이닝 분야에 비해 접근이 어렵고 복잡해 기대에 비해 그 활용도가 높지 못한 실정이다. 이에 본 연구는 프로그래밍 언어나 고사양 하드웨어나 솔루션에 의존하지 않고, 쉽고 간편한 소셜 미디어 텍스트 마이닝을 위한 통합 애플리케이션으로 Korean Natural Language Application(KoALA)을 개발하고자 한다. KoALA는 소셜 미디어 텍스트 마이닝에 특화된 애플리케이션으로, 한글, 영문을 가리지 않고 분석 가능한 통합 애플리케이션이다. 데이터 수집에서 전처리, 분석, 그리고 시각화에 이르는 전 과정을 처리해준다. 본 논문에서는 디자인 사이언스(design science) 방법론을 활용해 KoALA 애플리케이션을 디자인, 구현, 적용하는 과정에 대해서 다룬다. 마지막으로 블록체인 비즈니스 관련 사례를 들어 KoALA의 실제 활용방안에 대해서 다룬다. 본 논문을 통해 소셜 미디어 텍스트 마이닝의 대중화와 다양한 도메인에서 텍스트 마이닝의 실무적, 학술적 활용을 기대해 본다.

• Asian Journal for Public Opinion Research
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• 제1권4호
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• pp.268-291
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• 2014

Social media is becoming more and more important in social movements. This study, adopting the web mining approach, attempts to investigate how social media, Facebook in particular, functioned in the "May 25 Protest" and the "May 27 Protest", two movements which broke out in Macao on 25 and 27 May 2014, respectively, against the Retirement Package Bill. In the two protests, Macao residents deployed Facebook to share information and motivated people's participation. Twelve events (181,106 people invited) and 36 groups/pages (41,266 members) related on Facebook were examined. Results showed that the information flow on Facebook fluctuated in accordance with the event development in reality. Multiple patterns of manifestation, such as video of adopted news or songs, designed profile (protest icon), original ironic pictures, self-organized clubs by undergraduates and white T-shirts as a symbol, among others, appeared online and interacted with offline actions. It was also found that social media assisted the information diffusion and provided persuasive reasons for netizens to join the movement. Social media helped to expand movement influence in providing a platform for diversified performances for actions taken in a protest, which could express and develop core and consistent movement repertoire.