• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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• pp.171-191
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• 2020

Purpose The study aims to compare the online review writing behavior of users in China and the United States through text mining on online reviews' text content. In particular, existing studies have verified that there are differences in online reviews between different cultures. Therefore, the purpose of this study is to compare the differences between reviews written by Chinese and American tourists by analyzing text contents of online reviews based on cultural theory. Design/methodology/approach This study collected and analyzed online review data for hotels, targeting Chinese and US tourists who visited Korea. Then, we analyzed review data through text mining like sentiment analysis and topic modeling analysis method based on previous research analysis. Findings The results showed that Chinese tourists gave higher ratings and relatively less negative ratings than American tourists. And American tourists have more negative sentiments and emotions in writing online reviews than Chinese tourists. Also, through the analysis results using topic modeling, it was confirmed that Chinese tourists mentioned more topics about the hotel location, room, and price, while American tourists mentioned more topics about hotel service. American tourists also mention more topics about hotels than Chinese tourists, indicating that American tourists tend to provide more information through online reviews.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Journal of Communications and Networks
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• 제12권4호
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

• 산업경영시스템학회지
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• 제44권4호
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• 한국콘텐츠학회논문지
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• 제9권8호
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• pp.272-284
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• 2009

본 논문에서는 텍스트마이닝 기술을 이용하여 온라인 고객리뷰를 분석하기 위한 방법론을 제안하였다. 온라인 고객리뷰를 보다 효율적이고 효과적으로 분석할 수 있도록 시장세분화의 개념을 도입하였다. 즉, 제안한 방법론은 텍스트마이닝 분야에서 시장세분화의 개념에 부응하는 기술들이라 할 수 있는 범주화와 정보추출 기법의 사용을 포함한다. 특히, 통계적으로 보다 견고한 분석결과를 도출할 수 있도록 전통적 통계분석기법중의 하나인 교차분석방법을 제안하는 방법론에 포함하였다. 제안한 방법론의 타당성을 확인하기 위하여 양질의 온라인 고객리뷰가 있는 웹사이트를 선정하여 실제로 온라인 고객리뷰들을 분석하여 보았다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권5호
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• pp.195-204
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• 2018

본 논문에서는 심층 합성 곱 신경망 모델 기반의 단-단계 물체 탐지기들의 탐지 성능을 향상시킬 수 있는 새로운 손실 함수와 온라인 고난도 예 마이닝 방식을 제안한다. 본 논문에서 제안하는 손실 함수와 온라인 고난도 예 마이닝 방식은 물체와 배경 간의 학습 데이터 불균형 문제를 해결할 뿐만 아니라, 각 물체의 위치 추정 정확도를 더 개선시킬 수 있다. 따라서 물체 탐지 속도가 빠른 단-단계 물체 탐지기들에 이-단계 물체 탐지기들과 비슷하거나 더 우수한 탐지 성능을 제공할 수 있다. PASCAL VOC 2007 벤치마크 데이터 집합을 이용한 다양한 실험들을 통해, 본 논문에서 제안하는 손실 함수와 온라인 고난도 예 마이닝 방식이 단-단계 물체 탐지기들의 성능 개선에 도움이 된다는 것을 입증해 보인다.

• 대한물리의학회지
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• 제14권3호
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• pp.55-62
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• 2019

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

• 한국의류학회지
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• 제47권4호
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• pp.729-742
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• 2023

This two-part study explores the changes in the types of perceived value and consumption channels for vintage clothing and the relationship between the two variables. In Study 1, we used text mining with the keyword "fashion+vintage." Emotional value was the most frequently mentioned, and environmental value increased the most. We also revealed an increasing trend in online channels for vintage clothing consumption. In Study 2, we analyzed 30 interviews with consumers who had purchased vintage clothing through online channels. We identified 7 attributes and 20 goals for vintage consumption online and pinpointed three strong connections. First, consumers reported high levels of service satisfaction due to the usefulness of algorithms. Second, the authenticity and heritage information available through online and mobile channels were associated with consumers' perceptions of value related to financial benefits. Third, consumers sought to find rare products through online channels, leading to a strong influence on their sense of achievement. Overall, this study proposed ways to increase the value of vintage clothing perceived by consumers through consumption online.

• 산업경영시스템학회지
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• 제44권2호
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• pp.66-77
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• 2021

Companies widely use survey to identify customer requirements, but the survey has some problems. First of all, the response is passive due to pre-designed questionnaire by companies which are the surveyor. Second, the surveyor needs to have good preliminary knowledge to improve the quality of the survey. On the other hand, text mining is an excellent way to compensate for the limitations of surveys. Recently, the importance of online review is steadily grown, and the enormous amount of text data has increased as Internet usage higher. Also, a technique to extract high-quality information from text data called Text Mining is improving. However, previous studies tend to focus on improving the accuracy of individual analytics techniques. This study proposes the methodology by combining several text mining techniques and has mainly three contributions. Firstly, able to extract information from text data without a preliminary design of the surveyor. Secondly, no need for prior knowledge to extract information. Lastly, this method provides quantitative sentiment score that can be used in decision-making.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.259-266
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• 2023

본 논문에서는 속성기반 오피니언 마이닝(ABOM)을 적용한 협업 필터링의 정확도 성능을 개선할 수 있는 알고리즘을 제안한다. 실험을 위해 국내 스마트폰 사용자의 스마트폰 앱에 대한 총 1,227건의 온라인 소비자 리뷰 데이터가 분석에 사용되었다. KKMA(꼬꼬마)분석기를 이용하여 형태소 분석 및 KOSAC를 사용하여 감성어 분석 후 LDA 토픽 모델링을 사용하여 속성 추출한 가중치 값을 부여한 리뷰별로 토픽 모델링 결과를 이용하여 협업필터링의 평점과 감성스코어의 평점을 합산한 평균값 정확도 오차를 계산한 통계모형 성능 평가인 MAE, MAPE, RMSE를 사용하였다. 실험을 통해 추천 알고리즘 중 전통적인 협업필터링과 LDA 속성 추출과 감성분석을 결합한 속성기반 오피니언 마이닝(Aspect-Based Opinion Mining, ABOM) 기법을 결합하여 온라인 고객의 앱 평점(APP_Score) 대한 정확도를 예측하였다. 분석 결과 전통적인 협업필터링을 구현한 평점의 정확도 보다 속성기반 오피니언 마이닝 CF를 적용한 평점의 예측 정확도가 더 우수한 것으로 나타났다.