• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.669-696
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• 2010

Assuming that the whole meaning of a document is a composition of the meanings of each part, this paper proposes to study the automatic grading of movie reviews which contain sentimental expressions. This will be accomplished by calculating the values of semantic segments and performing data classification for each review. The ARSSA(The Automatic Rating System for Sentiment analysis using an Appraisal dictionary) system is an effort to model decision making processes in a manner similar to that of the human mind. This aims to resolve the discontinuity between the numerical ranking and textual rationalization present in the binary structure of the current review rating system: {rate: review}. This model can be realized by performing analysis on the abstract menas extracted from each review. The performance of this system was experimentally calculated by performing a 10-fold Cross-Validation test of 1000 reviews obtained from the Naver Movie site. The system achieved an 85% F1 Score when compared to predefined values using a predefined appraisal dictionary.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.339-347
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• 2008

As a number of product information is increasing in online markets, customers can purchase products with no spatial and time problems. However, in case of an online market, since customers can't see products directly, others' reviews make a big influence to customers. Meanwhile, it is a burden to read all reviews about some products. Therefore, we need to provide refined information to customers as summarizing whole product reviews. In this paper, we explain about the product review summarization system which can provide to customers as show evaluation scores of product features. Natural Language Processing skills and computational statistics are utilized for summarization. Customers can get chances to buy a feasible product that he wants to get through this system. Moreover, Enterprises can find out the needs of customers deeply.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.81-89
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• 2020

Online reviews we encounter commonly on SNS, although a complex range of assessment information affecting the consumer's preferences are included, it is general that such information is just provided by simple numbers or star ratings. Based on those review types, it is not easy to get specific information that consumers want and use it to make a decision for purchase. Therefore, in this study, we propose a prediction methodology that can provide ratings broken down by evaluation items by performing sentiment analysis on restaurant reviews written in Korean. To this end, we select 'food', 'price', 'service', and 'atmosphere' as the main evaluation items of restaurants, and build a new sentiment dictionary for each evaluation item. It also classifies review sentences by rating item, predicts granular ratings through sentiment analysis, and provides additional information that consumers can use to make decisions. Finally, using MAE and RMSE as evaluation indicators it shows that the rating prediction accuracy of the proposed methodology has been improved than previous studies and presents the use case of proposed methodology.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.115-126
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• 2022

The purpose of this study is to try to IPA(Importance-Performance Analysis) by applying text mining approaches to user review data for korea mobile banking applications, and to derive priorities for improvement. User review data on mobile banking applications of korea commercial banks (Kookmin Bank, Shinhan Bank, Woori Bank, Hana Bank), local banks (Gyeongnam Bank, Busan Bank), and Internet banks (Kakao Bank, K-Bank, Toss) that gained from Google playstore were used. And LDA topic modeling, frequency analysis, and sentiment analysis were used to derive key attributes and measure the importance and satisfaction of each attribute. Result, although 'Authorizing service', 'Improvement of Function', 'Login', 'Speed/Connectivity', 'System/Update' and 'Banking Service' are relatively important attributes when users use mobile banking applications, their satisfaction is not at the average level, indicating that improvement is urgent.

• Journal of Technology Innovation
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• v.30 no.4
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• pp.29-56
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• 2022

This study is a review paper on the contents and policy direction of the 'Transformative regional innovation policy'. The transformative regional innovation policy is a policy that develops countermeasures against climate crisis, aging, and polarization with a vision of system transformation from the perspective of local residents. The structure of the study is as follows. First, it deals with the characteristics of transformative regional innovation policy theory, which is distinguished from existing regional innovation policies. Next, studies related to major elements of transformative regional innovation policies, types of system transformation, and industrial formation are reviewed. The elements that differentiate the transformative regional innovation policy from existing policies are summarized and issues to be addressed in order to develop future discussions are drawn.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.63-73
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• 2014

As the advance of WWW, unstructured data including texts are taking users' interests more and more. These unstructured data created by WWW users represent users' subjective opinions thus we can get very useful information such as users' personal tastes or perspectives from them if we analyze appropriately. In this paper, we provide various analysis efficiently for unstructured text documents by taking advantage of OLAP (On-Line Analytical Processing) multidimensional cube technology. OLAP cubes have been widely used for the multidimensional analysis for structured data such as simple alphabetic and numberic data but they didn't have used for unstructured data consisting of long texts. In order to provide multidimensional analysis for unstructured text data, however, Text Cube model has been proposed precently. It incorporates term frequency and inverted index as measurements to search and analyze text databases which play key roles in information retrieval. The primary goal of this paper is to apply this text cube model to a real data set from in an Internet site sharing hotel information and to provide multidimensional analysis for users' reviews on hotels written in texts. To achieve this goal, we first build text cubes for the hotel review data. By using the text cubes, we design and implement the system which provides multidimensional keyword search features to search and to analyze review texts on various dimensions. This system will be able to help users to get valuable guest-subjective summary information easily. Furthermore, this paper evaluats the proposed systems through various experiments and it reveals the effectiveness of the system.

• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.709-723
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• 2015

Since 2011, after beginning of the systematic study on STEAM education, South Korea has developed a number of related programs. At this point we see that this is the time to clarify the challenges. The purpose of this study is to clarify the characteristics of their education through the review of many domestic and foreign papers in order to propose the challenges of STEAM education of Korea. The results are as follows. First, the course of integration cannot be separated by the difference in superiority but should be separated in accordance with the purpose of integration. Second, curriculum integration such as STEAM education is characterized by the emphasis on horizontal linking than vertical depth fusion. Accordingly, the content knowledge and vertical linkages are inevitably weakened. In order to overcome this problem, the key concepts and features that were emphasized in the previous curriculum need to also be emphasized in STEAM training, and the comparative study on the core concept and function of each subjects should be preceded. Third, after looking upon the current situation of our country's fusion research and talent training, the limits and the challenges that need to be overcome has been suggested. Fourth, with the basis on research results, we offered an example of the approach on STEAM education which is applicable to the current situation and proposed the challenges and implications that need to be addressed in the STEAM education of Korea in educational contexts such as curriculum, teaching and learning, and evaluation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.1-8
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• 2019

Recently, consumer distrust has deepened due to blog posts focusing only on public relations due to 'viral marketing'. In addition, marketing projects such as false writing or exaggerated use of the latter phase are one of the most popular programs in 2016 as they are cheaper and more effective than newspaper and TV ads, and the size of advertising costs is set to be a major means of advertising at '3 trillion 394.1 billion won. From this 'viral marketing,' it has become an Internet environment that needs tools to filter information. The fake review filtering application ADDAVICHI presented in this paper extracts, analyzes, and presents blog keywords, total number of searches, reliability and satisfaction when users search for content such as "event" and "taste restaurant." Reliability shows the number of ad posts on a blog, the total number of posts, and satisfaction shows a clean post with confidence divided into positive and negative posts. Finally, the keyword shows a list of the top three words in the review from a positive post. In this way, it helps users interpret information away from advertising.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.