• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.349-352
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• 2000

As a lot of people have used electronic commerce, many shopping malls have appeared on the Interne and the shopping information in them has been enormous. So, the need for a system to recommend product to customers is on the increase so as to reduce time and efforts for shopping. In this paper, we suppose a Product Recommender System which is constructed by applying data mining techniques to web for files and analyzing customer's action pattern, customer's profile and product purchase data. This system offers convenience that customers can get their desired information easily, by sending e-mail or mail and recommending web pages when they visit a shopping mall.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.8-15
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• 2003

Recently various of information suppliers provide information via WWW so the necessary of search engine grows larger. However the efficiency of most search engines is low comparatively because of using simple pattern match technique between user's query and web document. A specialized search engine returns the specialized information depend on each user's search goal. It is trend to develop specialized search engines in many countries. However, most such engines don't satisfy the user's needs. This paper proposes the specialized search engine for ocean information that uses user's query related with ocean and the association rules in web data mining can prove relation between web documents. So this search engine improved the recall of data and the precision in existent search method.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.139-146
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• 2008

It is very important to identify useful web user group and induce their behavior pattern in eCRM domain. Inducing user group with a similar inclination, a reliability of user group decreases because there is an uncertainty in online user data. In this paper, we have applied the 2-tier clustering, which uses the outcome of interaction with data from other tiers. Also we propose a method which induces user behavior pattern from a cluster and compare C4.5 with our method.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.113-121
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• 2019

The fourth industrial revolution technology focused on the fusion of infrastructure and various advanced technologies related city. Therefore, technical cooperation in various fields of research is essential. In order to activating the fourth industrial revolution technologies, it is necessary to research the state of technology in various fields. Consequently, this paper aims to analysis of domestic and foreign research trends on technology to the fourth industrial revolution using SNA and text mining for web site. We collected text, date data of research paper and report in web site for five years, that is, from January 1st in 2014 to December 31st in 2018. Next, we have deduced the major keywords in public data through analyzing the morphemes. Then we have analyzed the core and related keyword lists through an SNA. In Korea, the focus is on R&D and legal/institutional solution in relation to the fourth industrial revolution technology. On the other hand, in the case of foreign, there was focus on practical technologies for urban services in detail aspects.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.61-68
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• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.33-43
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• 2012

Semantic web is utilized to construct advanced information service by using semantic relationships between entities. Text mining can be applied to generate semantic relationships from unstructured data resources. In this study, ontology schema guideline, ontology instance generation, disambiguation of same name by text mining and advanced historical people finding service by reasoning have been proposed. Various relationships between historical event, organization, people, which are created by domain experts, are linked to literatures of National Institute of Korean History (NIKH). It improves the effectiveness of user access and proposes advanced people finding service based on relationships. In order to distinguish between people with the same name, we compares the structure and edge, nodes of personal social network. To provide additional information, external resources including thesaurus and web are linked to all of internal related resources as well.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.357-361
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• 2006

매일 새롭게 생기는 웹 페이지 수가 수천만 개, 온라인 문서들의 수가 수십억 개에 이르게 되자, 웹 사이트를 설계함에 있어서 웹 서버 로그 파일에 기록된 사용자의 행동을 분석하는 것이 중요한 부분이 되어가고 있다. 분석가들은 전체 웹 사이트에서 사용자 행동의 완전한 개요를 알기 원하기 때문에 고객이 방문했던 모든 다른 웹 서버를 통하여 사용자의 패스(path)를 다시 수집해야만 한다. 본 연구에서는 모든 로그 파일을 연결해서 방문했던 곳을 재구성하는 향상된 데이터 전처리 방법에 의하여 실험을 하여 로그 파일 크기를 감소시키게 되어 데이터 전처리의 성능이 향상되었음을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.152-154
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• 2000

World Wide Web(WWW) 데이터가 폭발적으로 증가하고 있는 시점에서, WWW의 데이터로부터 유용한 정보를 찾아내고 분석하는 일이 필요해졌다. 또한 WWW의 데이터만으로는 얻을 수 없는 기업의 의사결정을 위한 정보를 얻기 위해, 웹 페이지 접근 기록에서 얻어진 웹 로그기록들과 기업의 판매 트랜잭션 데이터베이스, 광고 데이터베이스 그리고 고객 정보를 통합하여 데이터 웨어하우스를 구축한다. 이러한 과정은 기업활동의 결과로 축적된 데이터 자원과 WWW의 데이터를 통합하여 체계적인 정보기반을 구축하고, 이러한 자원을 전략적으로 재활용하는 것이 목적이다. 본 논문에서는 WWW의 데이터와 기업의 데이터베이스를 통합하여 웨어하우스를 설계하고 여기에 데이터마이닝, OLAP을 적용하여 CRM에 활용하는 방안을 제안하고자 한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.301-306
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• 2000

최근 데이터 웨어하우징의 활발한 구축과 우수고객 확보를 위한 치열한 경쟁으로 데이터 마이닝은 많은 업체의 큰 관심을 끌고있다. 본 연구는 풍부한 알고리즘과 과학적 그래프를 제공하여 사용자로 하여금 최상의 데이터 마이닝 효과를 거둘 수 있도록 Statserver를 핵심 엔진으로 사용한 인터넷 기반의 데이터 마이닝 솔루션 개발에 관한 편이다