• Title/Summary/Keyword: Text Analytic

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Quantifying the Process of Patent Right Quality Evaluation : Combined Application of AHP, Text Mining and Regression Analysis (특허권리성의 정량적 평가방법에 대한 연구 : AHP, 텍스트 마이닝, 회귀분석의 활용)

  • Yoon, Janghyeok;Song, Jaeguk;Ryu, Tae-Kyu
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.38 no.2
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    • pp.17-30
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    • 2015
  • Technology-oriented national R&D programs produce intellectual property as their final result. Patents, as typical industrial intellectual property, are therefore considered an important factor when evaluating the outcome of R&D programs. Among the main components of patent evaluation, in particular, the patent right quality is a key component constituting patent value, together with marketability and usability. Current approaches for patent right quality evaluation rely mostly on intrinsic knowledge of patent attorneys, and the recent rapid increase of national R&D patents is making expert-based evaluation costly and time-consuming. Therefore, this study defines a hierarchy of patent right quality and then proposes how to quantify the evaluation process of patent right quality by combining text mining and regression analysis. This study will contribute to understanding of the systemic view of the patent right quality evaluation, as well as be an efficient aid for evaluating patents in R&D program assessment processes.

Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics (빅데이터 기반의 정성 정보를 활용한 부도 예측 모형 구축)

  • Jo, Nam-ok;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.33-56
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    • 2016
  • Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

Aesthetic's Influence on Ad Text for Hyper Connection Media and Consumers' Thinking Tendency (하이퍼 커넥션 미디어의 광고 텍스트유형과 사고방식에 따른 심미적 영향)

  • Park, Jinpyo;Kim, Jeayoung
    • Journal of the Korea Convergence Society
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    • v.11 no.3
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    • pp.171-179
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    • 2020
  • Media technologies that have made the hyper-connected age change the way people use communication text. Ads texts actively used by companies are storytelling and storydoing. These two advertising texts are very effective in inducing people's emotions and forming participatory behavior. People's thinking tendency also influence persuasion. The results of this study are as follows according to the type of ads text and the thinking tendency of consumers. Consumers' attitudes toward ads turned out to be more positive in synthetic thinking. In analytical thinking, the storytelling ads texts induced more favorable responses. On the other hand, in comprehensive thinking, the story doing text was effective. The same result was found in the perception of premium value, willingness to pay premium price, and repurchase intention.

Geriatric Dwelling Depression Measurement Based on Projective Image Analysis Modeling

  • Lee, Yewon;Park, Chongwook;Woo, Sungju
    • International Journal of Advanced Culture Technology
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    • v.6 no.4
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    • pp.323-330
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    • 2018
  • The growth of the older population is expected to further increase social problems associated with population aging, such as isolation, poverty, and depression. The emerging issues associated with the older population are also expected to provide further momentum on studies about the dwelling environment as factors that ensure the health of older people as well as improve their quality of life. Therefore, approaches for explaining the issues of the older age group should be diversified using a variety of factors and appropriate analytic tools. Studies on measuring depression have principally focused on assessing an objective self-report questionnaire, usually in a highly structured, textual form which may not reflect the cognitive impairment of older adults. The aim of this study was to define and measure dwelling depression among older adults in Korea. There are two specific hypotheses in this study as follows: (a) there will be statistically significant relationships with dwelling dissatisfaction and depression, and (b) dwelling depression tools containing text and images will be, respectively, assessment tools that have a good construct with content validity and reliability. In the first experiment, to define and measure dwelling depression, 301 people over 65 years old living in single and two-person households were surveyed using a text-based dwelling depression questionnaires from September 1-30, 2017. In the second experiment, to examine whether the projective image questionnaire could serve as a suitable replacement for the text-based questionnaires, the same participants were surveyed from January 22 to February 2, 2018. The results show that depression has a close correlation with dwelling dissatisfaction. In addition, the geriatric dwelling depression index (GDDI) based on the projective image was refined. Additionally, the projective image questionnaire has a close correlation with the text-based questionnaire. Finally, through ROC curve analysis, it was found that the projective image questionnaire can accurately predict a depression group. To this end, this preliminary study examined the validity of the projective image questionnaire in older adults to make this instrument feasible for older populations and to contribute to a profound understanding of geriatric depression due to the living environment. We hope they will provide a basis for further research on psychological diagnoses using projective images.

Development of Novel Disaster Pictogram Emergency Alert Technology for Hearing Impaired (청각장애인을 위한 재난안전 픽토그램 긴급알림 전달 기술 개발)

  • Yong-Yook Kim;Hyun-Chul Kim;Beom-Jun Cho
    • Journal of the Society of Disaster Information
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    • v.19 no.1
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    • pp.76-83
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    • 2023
  • Purpose: In emergency situations such as earthquakes, heavy rains, typhoons, or fires, when quick delivery of emergency alerts is crucial, the hearing impaired are the ones who are the most disadvantaged and vulnerable when alerts are only delivered through auditory or text alerts. They can't perceive auditory information, and many have difficulties in fast understanding text-based alerts. Method: An alert system that can deliver pictograms for specific disaster situations has been devised. Then, a novel approach based on artificial intelligence has been studied so that the pictograms for specific disaster situations can be chosen instantly once a disaster alert is issued in text. Result: A disaster alert system that delivers pictograms for specific disaster situations was developed and a novel method has been suggested for automatic delivery. Conclusion: A system to instantaneously deliver disaster alert information in pictograms has been developed to improve alert delivery to the populations vulnerable to disaster due to hearing impairment by the instantaneous understanding of disaster situations through visual information.

Analysis of an Effective Network of Information Delivery for Supporting Kill Chain in the Joint Battlefield Environment (합동전장 환경에서 효과적인 Kill Chain 지원을 위한 표적정보전달 네트워크 분석)

  • Lee, Chul-Hwa;Lee, Jong-Kwan;Goo, Ja-Youl;Lim, Jea-Sung
    • Journal of Information Technology and Architecture
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    • v.11 no.1
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    • pp.11-23
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    • 2014
  • Kill Chain is getting attention due to North Korea's recent nuclear test and missile launches and has emerged the need for an early build up. In order to build a materialized kill chain, you should review the unique kill chain to support operations effectively using various sensors and striking weapon system. Especially, you need a suitable network to reduce a reaction time against the enemy attack under joint operations environment etc. Currently there are many communication ways(e.g. data link, voice, video and text message) used in operations through satellite, wired and wireless and so on. Now, this paper contains analysis on various means for target information exchange which are used in the kill chain. And appropriate network of the kill chain for target information transmission is addressed to confirm feasibility of its alternatives, which is developed using AHP(Analytic Hierarchy Process). Finally, this paper is suggesting network and means of its building up for target information transmission of kill chain which can be implemented under the situation of joint battle field.

Book Genre Visualization based on Genre Identification Algorithm (장르 판별 알고리즘을 이용한 책 장르 시각화)

  • Kim, Hyo-Young;Park, Jin-Wan
    • The Journal of the Korea Contents Association
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    • v.12 no.5
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    • pp.52-61
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    • 2012
  • Text visualization is one of sectors in data visualization. This study is on methods to visually represent text's contents, structure, and form aspects based on various analytic techniques about wide range of text data. In this study -as a text visualization study-, 1) a method to find out the characteristics of a book's genre using words in the text of the book was looked into, 2) elements of visualization of a book's genre based on verification through an experiment were drew, and 3) the ways to intuitionally and efficiently visualize this were explained. According to visualization suggested by this study, first, actual genre of a book can be understood based on words used in the book. Second, with which genre is closed to the book can be found out with one glance through images of visualization. Moreover, the characteristics of complicated genres included in a book can be understood. Furthermore, the level of closeness (similarity) of a genre -which is found to be a representative genre using the number of dots, curvature of a curve, and brightness in the image- can be assumed. Finally, the outcome of this study can be used for a variety of fields including book customizing service such as a book recommendation system that provides images of personal preference books or genres through application of books favored by individual customers.

Fire Accident Analysis of Hazardous Materials Using Data Analytics (Data Analytics를 활용한 위험물 화재사고 분석)

  • Shin, Eun-Ji;Koh, Moon-Soo;Shin, Dongil
    • Journal of the Korean Institute of Gas
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    • v.24 no.5
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    • pp.47-55
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    • 2020
  • Hazardous materials accidents are not limited to the leakage of the material, but if the early response is not appropriate, it can lead to a fire or an explosion, which increases the scale of the damage. However, as the 4th industrial revolution and the rise of the big data era are being discussed, systematic analysis of hazardous materials accidents based on new techniques has not been attempted, but simple statistics are being collected. In this study, we perform the systematic analysis, using machine learning, on the fire accident data for the past 11 years (2008 ~ 2018), accumulated by the National Fire Service. The analysis results are visualized and presented through text mining analysis, and the possibility of developing a damage-scale prediction model is explored by applying the regression analysis method, using the main factors present in the hazardous materials fire accident data.

Safeguarding Korean Export Trade through Social Media-Driven Risk Identification and Characterization

  • Sithipolvanichgul, Juthamon;Abrahams, Alan S.;Goldberg, David M.;Zaman, Nohel;Baghersad, Milad;Nasri, Leila;Ractham, Peter
    • Journal of Korea Trade
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    • v.24 no.8
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    • pp.39-62
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    • 2020
  • Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

Assessing the Impact of Digital Procurement via Mobile Phone on the Agribusiness of Rural Bangladesh: A Decision-analytic Approach

  • Alam, Md. Mahbubul;Wagner, Christian
    • Agribusiness and Information Management
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    • v.5 no.1
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    • pp.31-41
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    • 2013
  • The research assesses the impact of a digital procurement (e-purjee) system for sugarcane growers in Bangladesh. The system itself is simple, transmitting purchase orders to local farmers via SMS text notification. It replaces a traditional paper-based system fraught with low reliability and delivery delays. Applying expected value theory, and using decision tree representations to depict growers' decision-making complexity in an information-asymmetric environment, we compute outcomes for the strategies and sub-strategies of ICT vs. traditional paper-based order management from the sugarcane growers' perspective. The study results show that the digital procurement system outperforms the paper-based system by tangibly reducing growers' economic losses. The digital system also appears to benefit growers non-monetarily, because of reduced uncertainty and a higher level of perceived fairness. Sugarcane growers appear to value the non-monetary benefits even higher than the economic advantages of the e-purjee system.

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