• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• The Journal of Asian Finance, Economics and Business
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• 제9권8호
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• pp.89-97
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• 2022

The e-commerce market faces significant credit risks due to the complexity of the industry and information asymmetries. Therefore, credit risk has started to stymie the growth of e-commerce. However, there is no reliable system for evaluating the creditworthiness of e-commerce companies. Therefore, this paper constructs a credit risk evaluation index system that comprehensively considers the online and offline behavior of online retail enterprises, including 15 indicators that reflect online credit risk and 15 indicators that reflect offline credit risk. This paper establishes an integration method based on a fuzzy integral support vector machine, which takes the factor analysis results of the credit risk evaluation index system of online retail enterprises as the input and the credit risk evaluation results of online retail enterprises as the output. The classification results of each sub-classifier and the importance of each sub-classifier decision to the final decision have been taken into account in this method. Select the sample data of 1500 online retail loan customers from a bank to test the model. The empirical results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy, which provides a basis for banks to establish a reliable evaluation system.

• 한국도서관정보학회지
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• 제39권3호
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• pp.321-344
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• 2008

이 연구는 어린이자료의 분류시스템 구축을 위한 한 방안으로 어린이들이 즐겨 찾는 국내 인터넷 포털과 어린이전문 인터넷 사이트의 디렉토리 구분 및 계층성과 인터넷서점의 어린이도서에 대한 항목구분과 계층성을 비교분석하였다. 이를 토대로 인터넷 포털에서 체계적이고 효율적인 어린이자료의 분류체계를 구성하기 위한 몇 가지 지침을 제시한 바, 그 내용은 다음과 같다. (1) 어린이네티즌들의 정보요구와 이용행태를 반영해야 한다. (2) 어린이들의 관점과 표현에 따른 용어를 선정하고 연령별 기준을 제시할 필요가 있다. (3) 이용자의 접근성과 편의성을 위해 명확한 계층성과 군집성을 유지해야 한다. (4) 주제나 개념중심의 카테고리에 어린이들의 활동과 대상을 보완하는 절충방식의 카테고리를 설정하는 것이 바람직할 것이다. (5) 교과목중심의 카테고리에 상상력과 호기심을 지속적으로 충족시켜 줄 수 있는 카테고리를 설정하고 상세한 주제별 세분을 추가할 필요가 있다.

• 한국지구과학회:학술대회논문집
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• 한국지구과학회 2006년도 춘계학술발표회 논문집
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• pp.93-93
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• 2006

• 지능정보연구
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• 제25권4호
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• pp.53-65
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• 2019

영상 기반의 보안 시스템의 증가함에 따라 각 용도마다 다른 다양한 객체들에 대한 처리들이 중요해지고 있다. 객체 추적은 객체 인식, 검출과 같은 작업들과 함께 필수적인 작업으로 다뤄진다. 이 객체 추적을 달성하기 위해서 다양한 머신러닝이 적용될 수 있다. 성공적인 분류기로써 전체 에러율 최소화(total-error-rate minimization) 기반의 방법론이 사용될 수 있다. 이 전체 에러율 최소화 기반의 방법론은 오프라인 학습을 기반으로 하고 있다. 객체 추적은 실시간으로 처리하며 갱신해야하는 것이 필수적이므로 온라인 학습(online learning)을 기반으로 하는 것이 적합하다. 온라인 전체 에러율 최소화 방법론이 개발되었지만 점근적으로 재가중되는(approximately reweighted) 작업이 포함되어 에러를 누적시킬 수 있다는 단점이 있다. 본 논문에서는 정확하게 재가중되는(exactly reweighted) 방법론을 제안하면서 온라인 전체 에러율 최소화가 달성되었다. 이 제안된 온라인 학습 방법론을 객체 추적에 적용하여 총 8개의 데이터베이스에서 다른 추적 방법론들 보다 좋은 성능이 달성되었다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of the Korean Data and Information Science Society
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• 제26권4호
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• pp.985-995
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• 2015

Add-on selling can provide new chances to increase sellers' profits and meet customers' needs. Although prior studies have advocated add-on selling for its business value, there is an argument that add-on selling can cause customer repulsion. Therefore, we need to understand customer purchasing pattern related to add-on selling in order to promote it and to mitigate the customer repulsion. To that end, we applied data mining techniques to the 24,925 transactions of data from an online open market in Korea. We then conducted feature selection to investigate the most influential factors that can explain the characteristics of add-on selling transactions using a classification model. We also identified association rules among add-on selling and promotions. Finally, based on the findings in our experiments, we proposed add-on selling strategies for the target online market.

• 디지털융복합연구
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• 제16권9호
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• pp.159-172
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• 2018

이용자의 정보를 수집하여 활용하는 온라인 트래킹 기술이 빠르게 발전하고 있다. 온라인 트래킹은 제품과 서비스 질의 향성, 이용자 경험의 증진의 측면에서 그 필요성이 강조되지만, 이용자의 프라이버시 침해나 정보 보안 취약성 등의 문제를 내포하고 있다. 이에 본 연구에서는 온라인 트래킹 기술들을 탐색하고, 이를 통해 온라인 트래킹과 관련한 정책 수립시, 고려해야 할 사항을 파악하고자 하였다. 그 결과, 온라인 트래킹 기술은 '일반쿠키', '슈퍼쿠키', '핑거프린팅', '디바이스 ID 트래킹', '크로스 디바이스 트래킹' 등으로 구분되었다. 온라인 트래킹의 발생 단계, 기술 생성주체, 활용목적, 정보의 유지기간 및 저장형식, 기술의 변화 등이 정책적으로 고려되어야 할 사항인 것으로 나타났다. 정책입안자와 산업관계자는 온라인 트래킹 기술의 특성에 따라 이용자가 인지하는 위험 정도가 다를 수 있음을 인지해야 한다. 그리고 온라인 트래킹 기술의 분류에 영향을 미칠 수 있는 다양한 요인에 대한 정책적인 이해가 필요하다. 마지막으로 산업계에서는 통합적 프라이버시 시스템을 구축 등의 선제적 대응이 필요하다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.