• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.875-882
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• 2008

This research proposes an alternative approach to machine learning based ones for text categorization. For using machine learning based approaches for any task of text mining, documents should be encoded into numerical vectors; it causes two problems: huge dimensionality and sparse distribution. Although there are various tasks of text mining such as text categorization, text clustering, and text summarization, the scope of this research is restricted to text categorization. The idea of this research is to avoid the two problems by encoding a document or documents into a table, instead of numerical vectors. Therefore, the goal of this research is to improve the performance of text categorization by proposing approaches, which are free from the two problems.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.35-39
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• 2002

광고성 메일이 개인 당 하루 평균 10통 내외로 오며, 그 제목만으로는 광고메일을 효율적으로 제거하기 어려운 현실이다. 이러한 어려움은 주로 광고 제목을 교묘히 인사말이나 답신처럼 변경하는 데에서 오는 것이며, 이처럼 제목으로 광고를 삭제할 수 없도록 은폐하는 노력은 계속될 추세이다. 그래서 제목을 통한 변화에 적응하면서, 제목뿐만 아니라 내용에 대한 의미 파악을 자동으로 수행하여 스팸 메일을 차단하는 방법이 필요하다. 본 연구에서는 정상 메일과 스팸 메일의 범주화(classification) 방식으로 접근하였다. 이러한 범주화 방식에 대한 기준을 자동으로 알기 위해서는 사람처럼 문장 해독을 통한 의미파악이 필요하지만, 기계가 문장 해독을 통해서 의미파악을 하는 비용이 막대하므로, 의미파악을 단어수준 등에서 효율적으로 대신하는 text mining과 web contents mining 기법들에 대한 적용 및 비교 연구를 수행하였다. 약 500 통에 달하는 광고메일을 표본으로 하였으며, 정상적인 편지군(500 통)에 대해서 동일한 기법을 적용시켜 false alarm도 측정하였다. 비교 연구 결과에 의하면, 메일 패턴의 가변성이 너무 커서 wrapper generation 방법으로는 해결하기 힘들었고, association rule analysis와 link analysis 기법이 보다 우수한 것으로 평가되었다.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.27-32
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• 2005

Recently there has been much interest in applying data mining to computer network intrusion detection. A new approach, based on the k-Nearest Neighbour(kNN) classifier, is used to classify Program behaviour as normal or intrusive. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a Popular method in text mining. A simple example illustrates the proposed procedure.

• English & American cultural studies
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• v.17 no.1
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• pp.81-106
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• 2017

Using recently emerging quantitative methods, this article provides a comparative study of the diachronic changes in the narrations of novel, history, and science from the early 18th-century to the 20th-century. To trace the narrative changes in different genres, this article discusses how text-mining methodology can be introduced in literary studies. We compared the traces of narrative in three genres—novel, history, and science—as a pilot study, with the three major grammatical elements of narrative: pronoun, subordinating conjunction, and action verbs in past tense. The results of data-mining show that the use of pronoun and action verb has increased in the genre of novel toward the $20^{th}$ century, while history and science has developed less story-like writing styles.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.581-590
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• 2024

This study aims to extract key topics through text mining of incident records (incident history, post-incident measures, preventive measures) from construction safety accident case data available on the public data portal. It also seeks to provide fundamental insights contributing to the establishment of manuals for disaster prevention by identifying correlations between these topics. After pre-processing the input data, we used the LDA-based topic modeling technique to derive the main topics. Consequently, we obtained five topics related to incident history, and four topics each related to post-incident measures and preventive measures. Although no dominant patterns emerged from the topic pattern analysis, the study holds significance as it provides quantitative information on the follow-up actions related to the incident history, thereby suggesting practical implications for the establishment of a preventive decision-making system through the linkage between accident history and subsequent measures for reccurrence prevention.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.59-68
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• 2011

As the role of soccer game analyst who analyzes soccer games and creates soccer wining strategies is emphasized, it is required to have high-level analysis beyond the procedural ones such as main event detection in the context of IT based broadcasting soccer game research community. In this paper, we propose a novel approach to generate the high-level in-depth analysis results via real-time text based soccer Webcast and text mining. Proposed method creates a metadata such as attribute, action and event, build index, and then generate available knowledges via text mining techniques such as association rule mining, event growth index, and pathfinder network analysis using Webcast and domain knowledges. We carried out a feasibility experiment on the proposed technique with the Webcast text about Spain team's 2010 World Cup games.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.217-234
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• 2018

In this study, big data analysis was conducted for domestic and international sports goods brands. Text Mining, TF-IDF, Opinion Mining, interestity graph were conducted through the social matrix program Textom and the fashion data analysis platform MISP. In order to examine the recent recognition of sports brands, the period of study is limited to 1 year from January 1, 2017 to December 31, 2017. As a result of analysis, first, we could confirm the products representing each brand. Second, I could confirm the marketing that represents each brand. Third, the common words extracted from each brand were identified. Fourth, the emotions of positive and negative of each brand were confirmed.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.294-301
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• 2004

·Text-mining approach to e-documents organization based on topic hierarchy - Machine-Learning ＆ information Theory-based ㆍ 'Category(topic) discovery' problem → document bundle-based user-constraint document clustering ㆍ 'Automatic categorization' problem → Accelerated EM with CU-based active learning → 'Hierarchy Construction' problem → Unsupervised learning of category subsumption relation