• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.37-40
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• 2019

On this paper, we show the way of forming graph data structure via setting an edge between Korean actors if they appeared in the same movie. From this graph, we calculate the 'centralities' (which declared on this paper) for each actor, then examine distribution by ranking the actors of the centralities and analyze the change of the actor who is/was center on the graph by years. Finally, we suggest the way that sets the numerically Range limits on social group.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.193-201
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• 2011

This paper presents the holistic notion and model of an open social u-learning community, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad. The interactive social u-learning community design helps conceptualize and maximize advantages of ubiquitous environment in learning. The model is enabled by state-of-the-art web technologies; real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; and mining and analytics to assess learner outcomes. Hence, u-learning design is highly scalable yet interactive and engaging.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Information Systems Review
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• v.24 no.1
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• pp.157-181
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• 2022

As competition in the airline industry intensifies, effective airline service quality evaluation has become one of the main challenges. In particular, as big data analytics has been touted as a new research paradigm, new research on service quality measurement using online review analysis has been attempted. However, these studies do not use review titles for analysis, relyon supervised learning that requires a lot of human intervention in learning, and do not consider airline characteristics in classifying service quality dimensions.To overcome the limitations of existing studies, this study attempts to measure airlines service quality and to classify it into the AIRQUAL service quality dimension using online review text as well as title based on self-trainingand sentiment analysis. The results show the way of effective extracting service quality dimensions of AIRQUAL from online reviews, and find that each service quality dimension have a significant effect on service satisfaction. Furthermore, the effect of review title on service satisfaction is also found to be significant. This study sheds new light on service quality measurement in airline industry by using an advanced analytical approach to analyze effects of service quality on customer satisfaction. This study also helps managers who want to improve customer satisfaction by providing high quality service in airline industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.225-230
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• 2016

Recently, Web has arisen large amount of data that to the development of the network and the Internet. In order to process it appeared that Big Data technology. Big Data technologies have been studied aiming a multifaceted and accurate analysis using existing regular data and a variety of data social data. But social data does not have the expertise and objectivity. And such manipulation and concealment and distortion of information have been raised troubling. Thus, this paper proposes for trust big data platform and will be described in detail. The big data platform proposed in this paper consists of data refiner, Data Analyzer, co-truster, visualizer, searcher, etc.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.1-18
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• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1509-1521
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• 2011

WfMC, which is one of the international standardization organizations leading the business process and workflow technologies, has been officially released the BPAF1.0 that is a standard format to record process instances' event logs according as the business process intelligence mining technologies have recently issued in the business process and workflow literature. The business process mining technologies consist of two groups of algorithms and their analysis techniques; one is to rediscover flow-oriented process-intelligence, such as control-flow, data-flow, role-flow, and actor-flow intelligence, from process instances' event logs, and the other has something to do with rediscovering relation-oriented process-intelligence like process-driven social networks and process-driven affiliation networks from the event logs. The current standardized format of BPAF1.0 aims at only supporting the control-flow oriented process-intelligence mining techniques, and so it is unable to properly support the relation-oriented process-intelligence mining techniques. Therefore, this paper tries to extend the BPAF1.0 so as to reasonably support the relation-oriented process-intelligence mining techniques, and the extended BPAF is termed BPAF2.0. Particularly, we have a plan to standardize the extended BPAF2.0 as not only the national standard specifications through the e-Business project group of TTA, but also the international standard specifications of WfMC.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.712-720
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• 2022

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.