• Journal of Digital Convergence
• /
• v.18 no.2
• /
• pp.367-376
• /
• 2020

According to recent statistics on new consumer market trends, 'alone consumption' is at the center. This study focuses on the social big data that attracts the public's opinions in that it is important for a certain social trend to comprehensively understand the various fields such as society, locality, culture, marketing, economics, and psychology that form the background for it. Therefore, we set up the linkage of 'solo consumption' and conducted research on new consumer market trends using Opinion Analisys. As a result of this trend analysis, representative keywords such as 'honbab', 'honsul' and 'honyoeng' were derived and analyzed the trend of new consumer market using this data. Alone consumption is an inevitable new consumption trend caused by demographic change after the global economic crisis. The importance as a trend reflecting this will be further strengthened. Trend analysis by social big data will help scientific and systematic business distribution strategies and planning to help make new and valuable decisions and decisions about new consumer markets.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.3
• /
• pp.358-364
• /
• 2006

At present age, great many informations are no exaggeration to say that supply information of better quality to users depend on that grasp correctly user's query intention through internet along with fast development of internet. Therefore, this thesis suggest that generating meaning relation between keywords with that result by passing through morpheme analysis and syntactic analysis about Natural Language Query. This approach is implied more meaning relation than query by simple keyword or simple combination between keywords. Therefore, it is going to permit much more efficient information retrieval because of solving problem about existent query form, and generating query that user's query intention is reflected more correctly.

• Journal of Advanced Navigation Technology
• /
• v.20 no.6
• /
• pp.617-624
• /
• 2016

The hangul word embedding should be performed certainly process for noun extraction. Otherwise, it should be trained words that are not necessary, and it can not be derived efficient embedding results. In this paper, we propose model that can retrieve more efficiently by query language expansion using hangul word embedded, apriori, and text mining. The word embedding and apriori is a step expanding query language by extracting association words according to meaning and context for query language. The hangul text mining is a step of extracting similar answer and responding to the user using noun extraction, TF-IDF, and cosine similarity. The proposed model can improve accuracy of answer by learning the answer of specific domain and expanding high correlation query language. As future research, it needs to extract more correlation query language by analysis of user queries stored in database.

• Journal of Digital Convergence
• /
• v.11 no.7
• /
• pp.193-199
• /
• 2013

In this paper, a user search history based potential query recommendation system is proposed to enable the user of information search system to represent one's potential desire for information in terms of query and to facilitate the desired information to be searched. The proposed system has analyzed the association with the existing users's search histories based on the users' search query, and it has extracted the users's potential desire for information. The extracted potential desire for information is represented in terms of recommended query and thereby made recommendations to users. In order to analyze the effectiveness of the system proposed in this paper, we conducted behavioral experiments by using search histories of 27656. As a result of behavioral experiments, the experiment subjects were found to show a statistically significant higher level of satisfaction when using the proposed system as compared to using general search engines.

• Journal of the Korea Computer Industry Society
• /
• v.5 no.9
• /
• pp.1111-1118
• /
• 2004

Natural language query is the best user interface for the users of web text retrieval systems. This paper proposes a retrieval system with expanded keyword from syntactically-analyzed structures of user's natural language query based on natural language processing technique. Through the steps combining or splitting the compound nouns based on syntactic tree traversal, and expanding the other-formed or shorten-formed keyword into multiple keyword, it shows that precision and correctness of the retrieval system was enhanced.

• Proceedings of the Korean Society for Information Management Conference
• /
• 2006.08a
• /
• pp.233-238
• /
• 2006

본 연구에서는 단어의 의미연상을 이용하여 시소러스를 작성해봄으로써 탐색 시소러스 구축에 있어 단어연상검사법의 적용가능성을 살펴보았다. 문헌정보학 분야를 대상으로 단어연상검사를 실시한 후 자극어와 반응어간의 의미관계를 파악하고 반응어와 통제어휘를 비교 분석하였다. 실험 및 분석결과, 단어연상검사를 이용하면 다양한 연관관계 용어들을 시소러스에 포함시킬 수 있으며, 통제어휘집에 나타난 하위관계와 동등관계 용어들을 어느 정도 반영할 수 있다는 것을 확인하였다. 단어의 의미연상을 이용하여 구축된 탐색 시소러스는 정보검색환경에서 질의확장에 응용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.05a
• /
• pp.593-596
• /
• 2005

데이터마이닝은 대량의 실제 데이터로부터 이전에 잘 알려지지는 않았지만 묵시적이고 잠재적으로 유용한 정보를 추출하는 작업으로, 본 논문은 최근 인문학 정보 자료가 전산화되고 있는 가운데 대량의 정보와 특정 체계를 갖춘 ‘조선왕조실록’ 전산자료를 분석하고 기존의 단순한 정보 검색이 아닌 데이터마이닝 기법을 적용한 상세하고 예측가능 한 정보자료 추출법을 제시한다. 먼저 텍스트화 되어 있는 컨텐츠를 형태소분석기법을 사용하여 색인어를 추출하고 집계를 낸다. 질의어와 유관한 색인어의 군집정도와 출현시점을 분석하는데, 사용된 마이닝 기법은 연관규칙분석과 클러스터링 분석기법이다. 최종 결과치는 기존의 인문학연구 결과물과 비교하여 그 정확도를 분석해 보인다.

• Tunnel and Underground Space
• /
• v.25 no.4
• /
• pp.303-319
• /
• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.

• Journal of Korean Library and Information Science Society
• /
• v.50 no.1
• /
• pp.251-272
• /
• 2019

This study investigated the utilization and interrelatedness of Scopus subject categories. To conduct this study, major and minor subject categories of journals listed in the 2017 Scopus index were used. The results showed varying degrees of interrelatedness of subject categories. At the major subject category level, the utilization was the highest in Medicine, while Social Sciences showed a greater degree of interrelatedness in comparison to Medicine. Yet, at the minor subject level, 2700 General Medicine was particularly dominant in terms of utilization and interrelatedness. Moreover, co-occurrences of minor subject categories showed varying degrees of interrelatedness between pairs of minor subject categories. Pairs of minor subject categories showed the following characteristics: a) two subject categories having identical or closely identical descriptions, b) two different categories having an interrelationship by subject areas, and c) one category conceptually encompassing another category. Due to varying degrees of utilization and interrelatedness among subject categories, minor subject categories that may greatly influence the major subject categories in conducting research studies should be investigated in detail.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.873-876
• /
• 2017

최근 정보검색의 효율성을 위해 데이터를 분석하여 해당 데이터를 가장 잘 나타내는 연관단어를 추출 및 추천하는 연구가 활발히 이루어지고 있다. 현재 관련 연구들은 출현 빈도수를 사용하는 방법이나 LDA와 같은 기계학습 기법을 활용해 데이터를 분석하여 연관단어를 생성하는 방법을 제안하고 있다. 기계학습 기법은 결과 값을 찾는데 사용되는 특징들을 전문가가 직접 설계해야 하며 좋은 결과를 내는 적절한 특징을 찾을 때까지 많은 시간이 필요하다. 또한, 파라미터들을 직접 설정해야 하므로 많은 시간과 노력을 필요로 한다는 단점을 지닌다. 이러한 기계학습 기법의 단점을 극복하기 위해 인공신경망을 다층구조로 배치하여 데이터를 분석하는 딥러닝이 최근 각광받고 있다. 본 논문에서는 기존 기계학습 기법을 사용하는 연관단어 추출연구의 한계점을 극복하기 위해 딥러닝을 활용한다. 먼저, 인공신경망 기반 단어 벡터 생성기인 Word2Vec를 사용하여 다양한 텍스트 데이터들을 학습하고 룩업 테이블을 생성한다. 그 후, 생성된 룩업 테이블을 바탕으로 인공신경망의 한 종류인 합성곱 신경망을 활용하여 사용자가 입력한 주제어와 관련된 최근 뉴스데이터를 분석한 후, 주제별 최신 연관단어를 추출하는 시스템을 제안한다. 또한 제안한 시스템을 통해 생성된 연관단어의 정확률을 측정하여 성능을 평가하였다.