• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.400-405
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• 2018

본 연구는 한국어 화장품 리뷰 코퍼스의 자질기반 감성 분석을 위하여, 이 도메인에서 실현되는 중요한 다단어 표현(MWE)의 유한상태 그래프 사전과 문법을 구축하는 방법론을 제시하고, 실제 구축된 사전과 문법의 성능을 평가하는 것을 목표로 한다. 본 연구에서는 자연어처리(NLP)에서 중요한 화두로 논의되어 온 MWE의 어휘-통사적 특징을 부분문법 그래프(LGG)로 형식화하였다. 화장품 리뷰 코퍼스에 DECO 한국어 전자사전을 적용하여 어휘 빈도 통계를 획득하고 이에 대한 언어학적 분석을 통해 극성 MWE(Polarity-MWE)와 화제 MWE(Topic MWE)의 전체 네 가지 하위 범주를 분류하였다. 또한 각 모듈간의 상호관계에 대한 어휘-통사적 속성을 반복적으로 적용하는 이중 증식(double-propagation)을 통해 자원을 확장하였다. 이 과정을 통해 구축된 대용량 MWE 유한그래프 사전 DECO-MWE의 성능을 테스트한 결과 각각 0.844(Pol-MWE), 0.742(Top-MWE)의 조화평균을 보였다. 이를 통해 본 연구에서 제안하는 MWE 언어자원 구축 방법론이 다양한 도메인에서 활용될 수 있고 향후 자질기반 감성 분석에 중요한 자원이 될 것임을 확인하였다.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.364-369
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• 2008

이 연구는 정보와 지식을 효과적으로 시각화하기 위해 만들어진 개념적 맵(Conceptual map)들의 종류와 그 특성과 차이점을 분석하고 활용 방안으로서 디지털 스토리텔링으로의 적용 가능성과 방법에 대한 연구이다. 사회현상에 존재하는 다양한 정보에서 사용자가 원하는 정보를 검색하고 조직하기 위해 다이어그램, 그래프, 맵 등 정보시각화를 통한 여러 방법들이 사용되고 있다. 특히 이 중에서 맵을 이용한 시각화에 주목하는 이유는 수많은 정보와 지식을 기반으로 만들어진 개념적 지도가 정보와 지식을 표현해 줄 뿐만 아니라, 이들 사이의 관계를 조직해주는 데에도 효율적으로 사용되고 있기 때문이다. 이러한 정보시각화는 대량의 정보 속에서 사용자들이 찾고자 하는 정보를 빠르고 용이하게 찾을 수 있도록 도와준다. 또한, 일련의 스토리 라인을 갖고 있는 책의 시각화의 경우 등장인물과 그들 주변에서 일어나는 사건들의 관계를 형상화할 수 있다. 이러한 과정을 통해 만들어진 개념적 맵에서는 개개인이 알고 있는 이야기와 그에 관한 정보를 다른 사람들과 의사소통하며 그 정보와 지식들이 확장될 수도 있어, 이는 지식시각화의 좋은 활용사례가 될 수 있을 것이다. 본 연구에서는 대표적인 개념적 맵의 세 가지 종류인 개념맵(Concept map), 지식맵(Knowledge map), 토픽맵(Topic map)의 정의와 특성들을 살펴보고, 각각의 구성요소의 차이점을 비교하여 시각화 방법론을 제안하였다. 또한, 각 맵의 특성과 차이점을 이용해 루이스 캐롤(Lewis Carroll)의 <이상한 나라의 엘리스>의 이야기 요소들을 각각의 개념적 맵들로 구성해봄으로써, 그 효과를 확인해보았다. 스토리텔링을 개념적 맵을 이용하여 표현할 경우, 사용자들은 스토리텔링을 효과적으로 접근할 수 있으며, 이러한 분석은 개념적 맵을 제작할 때 가이드라인으로 활용될 수 있을 것이다.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.89-96
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• 2016

Most data generated in construction projects is unstructured text data. Unstructured data analysis is very needed in order for effective analysis on large amounts of text-based documents, such as contracts, specifications, and RFI. This study analysed previously performed project's bid related documents (bidder inquiry) in overseas construction projects; as a results of the analysis frequent words in documents, association rules among the words, and various document topics were derived. This study suggests effective text analysis approach for massive documents with short time using text mining technique, and this approach is expected to extend the unstructured text data analysis in construction industry.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.41-47
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• 2008

There exists a many-to-many mapping relationship between persons and their names. A person may have multiple names, and different persons may share the same name. These synonymous and homonymous names may severely deteriorate the recall and precision of the person search, respectively. This study addresses the characteristics of features for resolving homonymous author names appearing in citation data. As disambiguation features, previous works have employed citation-internal features such as co-authorship, titles of articles, titles of publications as well as citation-external features such as emails, affiliations, Web evidences. To the best of our knowledge, however, there has been no literature to deal with the influences of features on author disambiguation. This study analyzes the effect of individual features on author resolution using a large-scale test set for Korean.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.171-187
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• 2019

The study aims to analyze the posts of depression-related Facebook groups to understand major topics discussed by group users. Specifically, the purpose of the study is to identify the topics and keywords of the posts to understand what users discuss about depression. Depression is a mental disorder that is somewhat sensitive in the online community, which is characterized by accessibility, openness and anonymity. The researchers have implemented a natural language-based data analysis framework that includes components ranging from Facebook data collection to the automated extraction of topics. Using the framework, we collected and analyzed 885 posts created in the past one year from the largest Facebook depression group. To derive more complete and accurate topics, we combined both automated and manual (e.g., stop words removal, topic size determination) methods. Results indicate that users discuss a variety of topics including depression in general, human relations, mood and feeling, depression symptoms, suicide, medical references, family and etc.

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.13-23
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• 2021

Purpose: This study aimed to derive clues for the franchise industry to overcome difficulties such as various legal regulations and social responsibility demands and to continuously develop by analyzing the research trends related to franchises published in Korea. Research design, data and methodology: As a result of searching for 'franchise' in ScienceON, abstracts were collected from papers published in domestic academic journals from 1994 to June 2021. Keywords were extracted from the abstracts of 1,110 valid papers, and after preprocessing, keyword analysis, TF-IDF analysis, and topic modeling using LDA algorithm, along with trend analysis of the top 20 words in TF-IDF by year group was carried out using the R-package. Results: As a result of keyword analysis, it was found that businesses and brands were the subjects of research related to franchises, and interest in service and satisfaction was considerable, and food and coffee were prominently studied as industries. As a result of TF-IDF calculation, it was found that brand, satisfaction, franchisor, and coffee were ranked at the top. As a result of LDA-based topic modeling, a total of 12 topics including "growth strategy" were derived and visualized with LDAvis. On the other hand, the areas of Topic 1 (growth strategy) and Topic 9 (organizational culture), Topic 4 (consumption experience) and Topic 6 (contribution and loyalty), Topic 7 (brand image) and Topic 10 (commercial area) overlap significantly. Finally, the trend analysis results for the top 20 keywords with high TF-IDF showed that 10 keywords such as quality, brand, food, and trust would be more utilized overall. Conclusions: Through the results of this study, the direction of interest in the franchise industry was confirmed, and it was found that it was necessary to find a clue for continuous growth through research in more diverse fields. And it was also considered an important finding to suggest a technique that can supplement the problems of topic trend analysis. Therefore, the results of this study show that researchers will gain significant insights from the perspectives related to the selection of research topics, and practitioners from the perspectives related to future franchise changes.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• Trans-
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• v.11
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• pp.97-116
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• 2021

This study aims to suggest an environment in which audience can play the role of micro-influencers after exploring the factors of satisfaction focusing on audiences who attended the Seoul Dance Film Festival(SeDaFF). In order to meet the research goal, among the audiences who attended SeDaFF, articles mentioning this festival on their SNS were collected and this data was analyzed using the LDA topic model. As a result, the most important satisfaction factor when visiting a dance film festival was the program. It might seem cliché to discuss the importance of programs at film festivals, but through the examination, this study made the case that if the satisfaction factor is met, it is still possible to influence the behavioral intentions and reinforcing the role of a micro-influencer even in a genre with a strong artistic nature and a limit to audience development. Furthermore, this study was intended to contribute to broadening the scope of research on the audience.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.60-66
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• 2021

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.