• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.381-389
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• 2022

With the collection of huge volumes of text, image, audio, video or combinations of these, in a word multimedia data, the need to explore them in order to discover possible new, unexpected and possibly valuable information for decision making was born. Starting from the already existing data mining, but not as its extension, multimedia mining appeared as a distinct field with increased complexity and many characteristic aspects. Later, the concept of big data was extended to multimedia, resulting in multimedia big data, which in turn attracted the multimedia big data mining process. This paper aims to survey multimedia data mining, starting from the general concept and following the transition from multimedia data mining to multimedia big data mining, through an up-to-date synthesis of works in the field, which is a novelty, from our best of knowledge.

• Genomics & Informatics
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• v.18 no.2
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• pp.15.1-15.6
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• 2020

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity "type 1 diabetes" in the phrase "type 1 and type 2 diabetes." This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE's existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.474-485
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• 2017

Sungnyemun Gate, Korea's National Treasure No.1, was destroyed by fire on February 10, 2008 and has been re-opened to the public again as of May 4, 2013 after a reconstruction work. Sungnyemun Gate become a national issue and draw public attention to be a major topic on news or research. In this research, text mining and association rule mining techniques were used on keyword of newspaper articles related to Sungnyemun Gate as a cultural heritage from 2002 to 2016 to find major keywords and keyword association rule. Next, we analyzed some typical and specific keywords that appear frequently and partially depending on before and after the fire and newpaper companies. Through this research, the trends and keywords of newspapers articles related to Sungnyemun Gate could be understood, and this research can be used as fundamental data about Sungnyemun Gate to information producer and consumer.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• Genomics & Informatics
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• v.2 no.3
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• pp.131-133
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• 2004

MediScore is an information retrieval system, which helps to search for the set of genes associated with a specific disease or the set of diseases associated with a specific gene. Despite recent improvement of natural language processing (NLP) and other text mining approaches to search for disease associated genes, many false positive results come out due to diversity of exceptional cases as well as ambiguities in gene names. In order to overcome the weak points of current text mining approaches, MediScore introduces statistical normalization based on binomial to normal distribution approximation which corrects inaccurate scores caused by common words not representing genes and interactive rescoring by the user to remove the false positive results. Interactive rescoring includes individual alias scoring for each gene to remove false gene synonyms, referring MEDLINE abstracts, and cross referencing between OMIM and other related information.

• The Journal of Bigdata
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• v.1 no.2
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• pp.35-44
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• 2016

Today there is a fierce competition between social commerce and multi-channel distribution in korea and it is need to do comparative analysis about success factors between social commerce and multi-channel distribution. Unlike the other studies that have only used survey method, this study analyzed the success factors between social commerce and multichannel distribution using text mining techniques. We expect that the result of the study not only gives the practical implication for making the competition strategy of the retailers but also contributes to the diverse extension research.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• Knowledge Management Research
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• v.19 no.2
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• pp.91-108
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• 2018

This study mainly focused on measuring the impact of comments for a particular song on the number of streamings and downloads. We modeled multiple regression equations to perform this analysis. We chose digital music market for the object of analysis because of its inherent characteristics, such as experience goods, high bandwagon effect, and so on. We carefully utilized text mining technique in accordance with the algorithm of Naïve Bayes classifier to distinguish whether a comment for a piece of music be regarded as positive or negative. In addition, we used 'size of agency' and 'existence of hit song' as moderating variables. The reason for usage of those variables is that those are assumed to affect users' decision for selecting particular song especially when downloading or streaming via music sites. We found empirical evidences that positive comments for a particular song increase the number of both downloads and streamings. However, positive comments may decrease the number of downloads when the size of agency of the artist is big. As a result, we were able to say that a positive comment for a particular song functioned as 'word-of-mouth' effect, inducing other users' behavioral response. We also found that other features of an artist such as size of the agency that the artist belongs to functioned as an external factor along with feature of the song itself.

• Knowledge Management Research
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• v.15 no.3
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.