• Cartoon and Animation Studies
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• s.44
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• pp.285-306
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• 2016

With the increase use of smartphones, users can consume contents such as webtoon, webnovel and TV drama directly provided by the producers. In this Direct-to-Consumer era, webdrama services from the portal websites are increasing rapidly. Webdramas such as , , and can be analyzed in real time using responses such as unique users, likes, and comments. The analyses used in this research were Social Media Big Data Mining Method and Opinion Mining Method. Specific key words from webdrama can be extracted and viewers positive, neutral or negative emotion can be predicted from the words. The analyses of popular webdramas showed that the established K-Pop Idol member appearance and servicing portal site greatly influence the views, traffics, comments, and likes. Also, 'Mobile TV' proved the effectiveness as another platform other than television. Mobile targeted contents and robust business models still to be developed and identified. Overcoming these few tasks, Korea will be proven to be a webdrama content powerhouse.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.93-94
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• 2015

본 논문에서는 학교 교육환경에서 청소년들에게 발생 할 수 있는소 셜미디어의 역기능을 빅 데이터 처리를 통하여 분석 할 수 있는 방법을 제시하고, 특히 악성 댓글을 위주로 한 청소년들 간의 소셜미디어를 중심으로 빅 데이터의 마이닝 기술을 활용하여 대표적인 청소년 문제의 확산을 방지 할 수 있는 시스템 제안한다.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• Human Ecology Research
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• v.60 no.1
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• pp.21-38
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• 2022

This study uses text mining compares and contrasts consumers' social media discourses on dietary related zero-waste movement before and after COVID-19. The results indicate that the amount of buzz on social networks for the zero- waste movement has been increasing after COVID-19. Additionally, the results of frequency analysis and topic modeling revealed that subjects associated with zero-waste movement were more diversified after COVID-19. Although the results of a sentiment analysis and word cloud visualization confirmed that consumers' positive responses toward the zero-waste have been increasing, they also revealed a need to educate and encourage those who are still not aware of the need for zero-waste. Finally, consumers mentioned only a small number of companies participating in zero-waste movement on SNS, indicating that the level of active involvement by such companies is much lower than that of consumers. Theoretical and educational implications as well as those for government policy-making are considered.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.133-140
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• 2020

Emotional analysis is an application of opinion mining that analyzes opinions and tendencies of people appearing in unstructured text. Recently, emotional analysis of social media has attracted attention, but social media contains newly-created words and slang, so it is not easy to analyze with existing emotional analysis. In this study, I design a new emotional analysis system to solve these problems. The proposed system is possible to analyze various emotions as well as positive and negative in social media including newly-created words and slang. First, I collect newly-created words and slang related to emotions that appear in social media. Then, expand the existing emotional model and use it to quantify the degree of sentiment in emotional words. Also, a new sentiment dictionary is constructed by reflecting the degree of sentiment. Finally, I design an emotional analysis system that applies an sentiment dictionary that includes newly-created words and an extended emotional model.

• Genomics & Informatics
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• v.18 no.2
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• pp.17.1-17.4
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• 2020

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

• The Journal of Industrial Distribution & Business
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• v.13 no.9
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• pp.37-50
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• 2022

Purpose: The purpose of this study is to explore and compare research trends in Korea and overseas academic papers on social media marketing, and to present new academic perspectives for the future direction in Korea. Research design, data and methodology: We used English abstract of research paper (Korea's: 1,349, overseas': 5,036) for word frequency analysis, topic modeling, and trend analysis for each topic. Results: The results of word frequency and co-occurrence frequency analysis showed that Korea researches focused on the experiential values of users, and overseas researches focused on platforms and content. Next, 13 topics and 12 topics for Korea and overseas researches were derived from topic modeling. And, trend analysis showed that Korean studies were different from overseas in applying marketing methods to specific industries and they were interested in the short-term performance of social media marketing. Conclusions: We found that the long-term strategies of social media marketing and academic interest in the overall industry will necessary in the future researches. Also, data mining techniques will necessary to generate more general results by quantifying various phenomena in reality. Finally, we expected that continuous and various academic approaches for volatile social media is effective to derive practical implications.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.457-464
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• 2020

This study aims to recognize customers' real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers' social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers' perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.1-6
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• 2023

Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.