• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.279-284
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• 2017

Currenty, in the era of big data, text mining and opinion mining have been used in many domains, and one of their most important research issues is to extract significant information from social media. Thus in this paper, we propose a logistic regression ensemble method of finding the main body text from blog HTML. First, we extract structural features and text features from blog HTML tags. Then we construct a classification model with logistic regression and ensemble that can decide whether any given tags involve main body text or not. One of our important findings is that the main body text can be found through 'depth' features extracted from HTML tags. In our experiment using diverse topics of blog data collected from the web, our tag classification model achieved 99% in terms of accuracy, and it recalled 80.5% of documents that have tags involving the main body text.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.335-342
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• 2015

The increasing interests on patents have led many individuals and companies to apply for many patents in various areas. Applied patents are stored in the forms of electronic documents. The search and categorization for these documents are issues of major fields in data mining. Especially, the keyword extraction by which we retrieve the representative keywords is important. Most of techniques for it is based on vector space model. But this model is simply based on frequency of terms in documents, gives them weights based on their frequency and selects the keywords according to the order of weights. However, this model has the limit that it cannot reflect the relations between keywords. This paper proposes the advanced way to extract the more representative keywords by overcoming this limit. In this way, the proposed model firstly prepares the candidate set using the vector model, then makes the graph which represents the relation in the pair of candidate keywords in the set and selects the keywords based on this relationship graph.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.531-534
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• 2005

Currently some problems are presented by the enlargement of network range and hardware upgrade for the solutions for network traffic and treatment speed of server processing, as well as the resource of networks and increasing speed of on-line information that is exceeding in operation limit of existing information systems. The study proposes the Architecture, an organic unification system of optimized content for retrieval, which is adapted to variable points of view of users or content changes of document aggregation by the study of algorithm, which offers easy retrieval of the location of documents on a multitude of on-line data.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• Ocean and Polar Research
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• v.41 no.2
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• pp.107-120
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• 2019

The frequency analysis of scientific terms using bibliographic information is a simple concept, but as relevant data become more widespread, manual analysis of all data is practically impossible or only possible to a very limited extent. In addition, as the scale of oceanographic research has expanded to become much more comprehensive and widespread, the allocation of research resources on various topics has become an important issue. In this study, the frequency analysis of scientific terms was performed using text mining. The data used in the analysis is a general-purpose scholarship database, totaling 2,878 articles. Hypoxia, which is an important issue in the marine environment, was selected as a research field and the frequencies of related words were analyzed. The most frequently used words were 'Organic matter', 'Bottom water', and 'Dead zone' and specific areas showed high frequency. The results of this research can be used as a basis for the allocation of research resources to the frequency of use of related terms in specific fields when planning a large research project represented by single word.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.153-171
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• 2019

Competition among cities has become fierce with decentralization and globalization, and each city tries to establish a brand image of the city to build its competitiveness and implement its policies based on it. At this time, surveys, expert interviews, etc. are commonly used to establish city brands. These methods are difficult to establish as sampling methods an empirical component, the biggest component of a city brand. In this paper, therefore, based on the precedent research's urban brand measurement and components, the words representing each city image property were extracted and relocated to five indicators to form the evaluation index. The constructed indicators have been validated through the review of three experts. Through the index, we analyzed the brands of four cities, Ulsan, Incheon, Yeosu, and Gyeongju, and identified the factors by using Topic Modeling and Word Cloud. This methodology is expected to reduce costs and monitor timely in identifying and analyzing urban brand images in the future.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.505-514
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• 2021

Among non-timber forest products, Tricholoma matsutake is a high value added item. Many countries, including Korea, China, and Japan, are doing research and technology development to increase artificial cultivation and productivity. However, the production of T. matsutake is on the decline due to global warming, abnormal temperatures and pine tree pest problems. Therefore, it is necessary to identify trends in domestic and foreign research on T. matsutake, respond to preemptive research and development to preserve the genetic resources of T. matsutake and increase its productivity. Based on the correlation between keywords in the high frequency keywords, it was observed that microbial clusters of T. matsutake are mainly found in Korea. The main focus in China has been the pharmacology studies on the ingredients of T. matsutake. The main focus in Japan has been on preserving the genetic diversity and species of T. matsutake. Thus, future domestic studies of T. matsutake will require pharmacological studies on the ingredients of T. matsutake and on its genetic diversity and species conservation. In addition, unlike China and Japan, genetic keywords did not appear in Korea at high frequency. Therefore, Korea will have to proceed with research using modern molecular biology techniques.

• Research in Community and Public Health Nursing
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• v.32 no.4
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• pp.467-476
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• 2021

Purpose: The purpose of this study is to understand the social perceptions of nurses in the context of the COVID-19 outbreak through analysis of media articles. Methods: Among the media articles reported from January 1st to September 30th, 2020, those containing the keywords '[corona or Wuhan pneumonia or covid] and [nurse or nursing]' are extracted. After the selection process, the text mining and topic modeling are performed on 454 media articles using textom version 4.5. Results: Frequency Top 30 keywords include 'Nurse', 'Corona', 'Isolation', 'Support', 'Shortage', 'Protective Clothing', and so on. Keywords that ranked high in Term Frequency-Inverse Document Frequency (TF-IDF) values are 'Daegu', 'President', 'Gwangju', 'manpower', and so on. As a result of the topic analysis, 10 topics are derived, such as 'Local infection', 'Dispatch of personnel', 'Message for thanks', and 'Delivery of one's heart'. Conclusion: Nurses are both the contributors and victims of COVID-19 prevention. The government and the nurses' community should make efforts to improve poor working conditions and manpower shortages.