• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.297-304
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• 2021

This study aims not only to access the visual thought-oriented approach that has been implemented in established art therapy and education but also to integrate language education and therapeutic approach to support the development of school-age children. Thus, text mining technique was applied to search for areas where different areas of language and art can be integrated. This research was conducted in accordance with the procedure of basic research, preliminary DB construction, text screening, DB pre-processing and confirmation, stop-words removing, text mining analysis and the deduction about the convergent areas. These results demonstrated that this study draws convergence areas related to regional, communication, and learning functions, areas related to problem solving and sensory organs, areas related to art and intelligence, areas related to information and communication, areas related to home and disability, topics, conceptualization, peer-related areas, integration, reorganization, attitudes. In conclusion, this study is meaningful in that it established a framework for designing an activity-centered convergence program of art and language in the future and attempted a holistic approach to support child development.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.203-209
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• 2021

In this study, a prototype design model was proposed for developing an online recruitment system through multi-dimensional data crawling and social media analysis, and validates text information and video interview in job application process. This study includes a comparative analysis process through text mining to verify the authenticity of job application paperwork and to effectively hire and allocate workers based on the potential job capability. Based on the prototype system, we conducted performance tests and analyzed the result for key performance indicators such as text mining accuracy and interview STT(speech to text) function recognition rate. If commercialized based on design specifications and prototype development results derived from this study, it may be expected to be utilized as the intelligent online recruitment system technology required in the public and private recruitment markets in the future.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.319-328
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• 2022

The purpose of this study was to investigate the relationship between depression due to the COVID-19 pandemic and park use behaviors using on line posts. During the period of the pandemic prevention activities, text data containing both 'park' and 'depression' were collected from blogs and cafes in the search engine of Naver and Daum, then analyzed using Text Mining and Social Network techniques. As a result, the main usage behaviors of park users who mentioned depression were 'look', 'stroll(walk)' and 'eat'. Other types of behaviors were connected centering around 'look', one of the communication behaviors. Also, from CONCOR analysis, as the cluster referred from communication behavior and dynamic behavior was formed as a single behavior type, it was considered park users with depression perceived the park as the space for communication and physical activities. As the spread of COVID-19 caused the restriction of communication activities, the users might consider parks as one of the solutions. In addition, it was considered that passive usage behaviors have prevailed rather than active ones due to the depression. Resulting outcomes would be useful to plan helpful urban park for citizens. It is necessary to further analyze the park use behavior of users in relation to the period of before/after the COVID-19 pandemic and the existence/nonexistence of depression.

• Journal of Information Technology Services
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• v.19 no.4
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• pp.85-107
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• 2020

The Internet of Things market, a technology that connects the Internet to various things, is growing day by day. Besides, various smart home services using IoT and AI (Artificial Intelligence) are being launched in homes. Related to this, existing smart home-related studies focus primarily on ICT technology, not on what service improvements should be made in customer positions. In this study, we will use smart home application customer review data to classify functional and design elements of smart home usability value and examine the ways customers think of service improvement. For this, LG Electronics and Samsung Electronics" Smart Home application, the main provider of Smart Home in Korea, customer reviews were crawled to conduct a comparative analysis between them. In this study, the review of IoT home-applications was analyzed to find service improvement insights from customer perspective, and related analysis of text mining, social network analysis and Doc2vec was used to efficiently analyze data equivalent to about 16,000 user reviews. Through this research, we hope that related companies effectively seek ways to improve smart home services that reflect customer needs and are expected to help them establish competitive strategies by identifying weaknesses and strengths among competitors.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1419-1425
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• 2017

The ultimate goal of legal knowledge search is to obtain optimal legal information based on laws and precedent. Text mining research is actively being undertaken to meet the needs of efficient retrieval from large scale data. A typical method is to use a word embedding algorithm based on Neural Net. This paper demonstrates how to search relevant information, applying Korean law information to word embedding. First, we extracts reference laws from precedents in order and takes reference laws as input of Law2Vec. The model learns a law by predicting its surrounding context law. The algorithm then moves over each law in the corpus and repeats the training step. After the training finished, we could infer the relationship between the laws via the embedding method. The search performance was evaluated based on precision and the recall rate which are computed from how closely the results are associated to the search terms. The test result proved that what this paper proposes is much more useful compared to existing systems utilizing only keyword search when it comes to extracting related laws.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.101-106
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• 2014

Information and Communication Technology environment has been developing rapidly and variety of services are in service. As data becomes increasingly sophisticated. These data was applied techniques of visualization in order to visualize efficiently. Various agencies are providing the map based data in real-time. However, traffic information is getting more complex and users are having a difficulty to understand with convential visualization techniques. This study was designed and implemented in the web service of traffic information using Focus+Context. Web service implemented HTML5(Hyper Text Markup Language 5), and it runs on browser of either desktop or mobile devices. This study sets an example as web application from a user perspective by combining information visualization and traffic information.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.147-154
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• 2017

This paper aims to build a home security system based on IoT to monitor a home on a mobile phone. The system consists of data gathering sensors, camera, gateway and Xively platform. The Raspberry Pi collects data from the three sensors and sends the data to Xively, and sends the video stream of home to a client in a smart phone through a internet. The servers are composed of Xively, socket server in Raspberry Pi and E-mail server in Google. The proposed system transmits e-mail, text message, and video stream when there are motion, fire, and gas leakage, and can control the gas valve through Raspberry Pi. The experimental results show that a user gets 'emergency E-mail' and text message and watches the video stream of the home through WIFI or LTE on a smart phone.

• The Journal of Society for e-Business Studies
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• v.21 no.4
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• pp.1-14
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• 2016

Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users' search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order 'document-by-concept' tensor (i.e., matrix), and each concept as the 2nd-order 'document-by-term' tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.275-282
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• 2020

Social interest on high-quality specialty coffee is increased due to customers' growing experience upon coffee and recent change of coffee culture, which is taking one step further from putting emphasis on not just price and quality but also psychological satisfaction. As a culture of drinking coffee and giving much value on its taste and flavor, a number of customers increasingly demand coffee which is probable to suit one's taste. Likewise, the number of specialty coffee shops is increasing with growing qualities of their coffee. Therefore, the purpose of this study is to analyze the main attributes of specialty coffee and to build a marketing system for specialty coffee shops. The text mining on domestic web portal sites by online big-data analysis is used to extract components of properties of specialty coffee and analyze the degree of how the elements affect the properties. According to the result of the study, words related to coffee taste, coffee beans and baristas were found to play a central role in the properties of specialty coffee.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.195-201
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• 2018

Smartphones and PCs have become essential components of our daily life. People are expressing their opinions freely in SNS by using these devices. We are able to predict public opinions on specific subject by analyzing the related big data in SNS. In this paper, we have collected opinion data in SNS and analyzed reputation by text mining in order to make a prediction for the will of the people before 19th presidential election in South Korea. The result shows that our method makes more accurate estimate than other election polls.