• Journal of the Korean Geotechnical Society
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• v.25 no.10
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• pp.67-76
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• 2009

Statistical analysis was performed using a series of data on RMR, RMR* and crown settlements collected from sites of weathering rock tunnels in Korea. The crown settlements were predicted by recurrence analysis, exponential function, and artificial neural network (ANN) using collected in-situ data. The result of the prediction fitted well compared to the measured settlement in the order of ANN, exponential function, and recurrence analysis. The range of crown settlement predicted by recurrence analysis widely scattered and promised larger settlement than the measured. Also in all method, the predicted value by RMR well matched compared to the measured settlement predicted by RMR*.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.537-546
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• 2014

The purpose of this study is to develope an algorithm which generates trip plans based on rating points of travel app users and travel experts to help potential travellers experience their desired destinations in advance. This algorithm uses the above rating points and the gradually created hierarchy to generate the most preferred and efficient trip courses. Users can go through video clips or panoramic VR videos of the actual destinations from their trip plans generated by the algorithm which may add excitement to their actual trips. With our heuristic methods, the more users input their ratings, the better trip plans can be generated. This algorithm has been tested on android OS and proven efficient in generating trip plans. This research introduces a way to experience travel destinations with panoramic VR video and proposes the algorithm which generates trip plans based on users' ratings. It is expected to be useful for travellers' trip planning and to contribute growth in the travel market.

• Journal of KIISE
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• v.42 no.10
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• pp.1286-1293
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• 2015

Most e-marketplaces build a reputation system that provides potential buyers with reputation scores of sellers in order for buyers to identify the sellers that are more reliable and trustworthy. The reputation scores are computed based on the aggregation of buyers' ratings. However, when these ratings are used to compute the reputation scores, the existing reputation systems do not make a distinction according to the following two criteria: the capability of the seller and the quality of an item. We claim that a reputation system needs to separate the two criteria in order to provide more precise information about the seller. In this paper, we propose a method to compute seller's reputation by separating the rating into the seller's score and the item's score. The proposed method computes the reputation of the seller's capability by using only the 'seller's score' and helps potential buyers to find reliable sellers who provide fast delivery and better service. In experiments, we propose a simulation strategy that reflects the real life of an E-marketplace and verify the effectiveness of our method by using the generated simulation data.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.597-607
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• 2013

This research examined factors in determining the success of theatrical U.S. animation movies. Based on movie characteristics and content factors, this study explore determinants of the success for animation movies. The results are follows: firstly, the success of animation movies were determined by some factors such as production expenses, the way of producing animation movies, sequential movies and viewer ratings. Secondly, there differences between the success in the U.S. and other countries. Although the success of animation movies in the U.S. were more production related factors, those in other countries were more quality related factors.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.10a
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• pp.225.1-225
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• 2003

잣을 이용한 쌀가루즉석죽 제조조건의 최적화와 제조조건에 따른 관능적 특성을 4차원 반응표면분석에 의하여 모니터링 하였다. 죽의 제조에 이용되는 잣은 가루같이 으깬 것을 첨가하는 것이 바람직하였다. 관능적 특성에 의한 잣죽의 점성에 대한 최적조건은 주입액의 양과 잣의 첨가비율이 각각 쌀가루 무게의 15.52배, 16%, 가열조리시간 7.79 분이었으며 이때 $R^2$는 0.9518, 관능평점은 7.45, 임계점은 maximum point였다. 색상에 대한 최적조건은 19.09배, 45%, 2.16분($R^2$=0.7917)이었고 관능평점은 5.44, saddle point였으며, 맛에 대한 최적조건은 12.90배, 22%, 11.40분($R^2$=0.9361), 관능평점은 7.11, maximum point였고, 향에 대한 최적조건은 15.06배, 20%, 4.9분($R^2$=0.8372), 관능평점은 6.16, saddle point였다. 관능적 특성을 모두 만족시켜주는 최적조건은 주입액의 양과 잣의 첨가비율이 각각 쌀가루무게의 16.86배와 15%, 가열조리시간 5.48분이었고 이때 $R^2$는 0.9738, 관능적 평점은 7.69, 임계점은 maximum point였다. 관능적 특성 중 점성은 맛과 전반적기호도에 큰 영향을 주었으며 상관계수는 각각 0.8173($R^2$=0.0001), 0.7878($R^2$=0.0002)이었고 맛은 향과 전반적인 기호도와 밀접한 관계가 있으며 상관계수는 각각 0.5196($R^2$=0.0325)과 0.7004($R^2$=0.0017)이었다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• 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 the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.231-234
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• 2000

본 연구는 오랜 시간에 거쳐 축적된 고객 데이터베이스를 활용하여 스코어링 방법을 적용할 수 있는 모델링의 개발에 목적이 있다. 기존의 전통적인 스코어링 방법은 인구 통계학적인 변수나 거래 관련 횡단면적인 자료를 이용하여 우량고객과 불량고객을 구분하는 판별분석의 형태가 대부분이다. 하지만 과거 고객에 대한 실적 자료가 시계열 형태를 이루며 존재하기 때문에 이에 대한 적절한 동태적 모형을 적용은 자연스러운 확장이라고 볼 수 있다. 본 연구에서 제안하는 모형은 고객들의 실적관련 시계열 자료를 GARCH 모형에 적합하여 미래의 실적 예측과 이에 대한 표준편차를 예측하여 하위 $10\%$에 해당하는 실적 예측치를 스코어링으로 하는 새로운 방법을 소개하고자 한다. 이 경우 스코어 값이 부호를 가지게 되므로 우량고객을 구분함과 동시에 큰 음수 값을 조사하여 위험 평점도 함께 측정할 수 있어서 실무 측면에서 유용하리라고 본다.