• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.545-545
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• 2008

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.427-428
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• 2021

정보통신기술의 발달로 전자상거래의 증가와 소비자들의 제품에 대한 경험과 지식의 공유가 활발하게 진행됨에 따라 소비자는 제품을 구매하기 위한 자료수집, 활용을 진행하고 있다. 따라서 기업은 다양한 기능들을 반영한 제품이 치열하게 경쟁하고 있는 현 시장에서 우위를 점하고자 소비자 리뷰를 분석하여 소비자의 정확한 소비자의 요구사항을 분석하여 제품기획 프로세스에 반영하고자 텍스트마이닝(Text Mining) 기술과 딥러닝(Deep Learning) 기술을 통한 연구가 이루어지고 있다. 본 논문의 기초자료가 되는 데이터셋은 포털사이트의 구매사이트와 오픈마켓 사이트의 소비자 리뷰를 웹크롤링하고 자연어처리하여 진행한다. 감성분석은 딥러닝기술 중 CNN(Convolutional Neural Network), LSTM(Long Short Term Memory) 조합의 모델을 구현한다. 이는 딥러닝을 이용한 제품기획 프로세스로 소비자 요구사항 반영, 경제적인 측면, 제품기획 시간단축 등 긍정적인 영향을 미칠 것으로 기대한다.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.23-33
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• 2023

Consumer review analysis is important for product development, customer satisfaction, competitive advantage, and effective marketing. Increased use of wireless earphones is expected to reach $45.7 billion by 2026 with growth in lifestyle. Therefore, in consideration of the growth and importance of the market, consumer reviews of wireless earphones from Apple and Samsung were analyzed. In this study, 11,320 wireless earphone reviews from Apple and Samsung sold on Coupang were collected to analyze consumers' purchase intentions and analyze consumer satisfaction through analysis of the frequency, sensitivity, and LDA topic model of text mining. As a result of topic modeling, 16 topics were derived and classified into sound quality, connection, shopping mall service, purchase intention, battery, delivery, and price. As a result of brand comparison, Samsung purchased a lot for gift purposes, had a high positive sentiment for price, and Apple had a high positive sentiment for battery, sound quality, connection, service, and delivery. The results of this study can be used as data for related industries as a result of research that can obtain improvements and insights on customer satisfaction, quality and market trends, including manufacturing, retail, marketers, and consumers.

• Information Systems Review
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• v.26 no.1
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• pp.337-353
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• 2024

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

• Information Systems Review
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• v.16 no.3
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• pp.113-134
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• 2014

Since a movie is an experience goods, purchase can be decided upon preliminary information and evaluation. There are ongoing researches on what impact online reviews might have on movie revenues. Whereas research in the past was focused on the effect of online reviews. The influence of online reviews appears to be significant in products like a movie because it is difficult to evaluate the feature prior to "consuming" the product. Since an online review is regarded to be objective, consumers find it more trustworthy. Contrary to prior research focused on movie review ratings and volume, we focus moves on movie features related specific reviews. This research proposes a predictive model for movie revenue generation. We decided 15 criteria to classify movie features collected from online reviews through the online review mining and made up feature keyword list each criterion. In addition, we performed data preprocessing and dimensional reduction for data mining through factor analysis. We suggest the movie revenue predictive model is tested using discriminant analysis. Following the discriminant analysis, we found that online review factors can be used to predict movie popularity and revenue stream. We also expect using this predictive model, marketers and strategic decision makers can allocate their resources in more parsimonious fashion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.79-81
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• 2014

People increasingly rely on sources of information from E-commerce reviews. Product reviews is an important determinant of potential customers' buying choices. They are also utilized by product manufacturers to find problems of their products and to collect competitive intelligence information about their competitors. Unfortunately, it is well-known that many online product reviews are not made by genuine costumers of products. Reviewers could write some undeserving positive reviews to promote or fake negative reviews to defame some certain product, and we call them fake product reviews. Fake product review detection makes an attempt to detect fake reviews and removes them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on fake product review detection. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect fake reviews are introduced and summarized. The trend of fake product review detection is concluded finally.

• Annual Conference of KIPS
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• 2009.11a
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• pp.735-736
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• 2009

사용자의 참여를 강조하는 Web2.0 시대를 맞이하여 개인의 블로그나 까페에 올라오는 무수히 많은 리뷰들이 실제 소비자의 마음을 움직이는 데에 많은 영향을 미치고 있다. 하지만 많은 리뷰들이 상당히 길게 작성되어 있기 때문에 원하는 정보만을 찾아내는 것은 어려운 일이다. 본 논문에서는 다양한 종류의 리뷰들 중에서도 많은 부분을 차지하고 있는 음식점에 관한 리뷰들을 분석하여 사용자가 원하는 정보를 요약하여 제공하는 방법을 제안한다. 이러한 방법을 통해서 사용자는 객관적인 판단을 내릴 수 있고, 시간적인 측면에서의 효율성을 획득할 수 있을 것이다.

• Knowledge Management Research
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• v.23 no.2
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• pp.229-251
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• 2022

Online reviews are useful for other consumers to make reasonable purchase decisions by providing previous buyers' experiences. However, when online reviewers are biased, online reviews do not accurately reflect the true quality of the product. Therefore, we investigated the characteristics of reviewers with underreporting bias to cope with the problem of declining reliability of online reviews. In this context, this study attempted to examine the characteristics of reviewers with underreporting bias using 14,165 reviews of Steam, an online game platform. As a result of the analysis, reviewers with underreporting bias mainly write reviews positively, write reviews within a short period from the game release date, but tend to write reviews after playing games for longer time, and write reviews when purchasing high-priced games. Since this study has explored the characteristics of reviewers showing underreporting bias, it will be meaningful as a basic study to cope with the problem caused by underreporting bias.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.181-188
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• 2018

In this paper, we propose an analysis scheme of customer spending pattern using text mining. In proposed consumption pattern analysis scheme, first we analyze user's rating similarity using Pearson correlation, second we analyze user's review similarity using TF-IDF cosine similarity, third we analyze the consistency of the rating and review using Sendiwordnet. And we select the nearest neighbors using rating similarity and review similarity, and provide the recommended list that is proper with consumption pattern. The precision of recommended list are 0.79 for the Pearson correlation, 0.73 for the TF-IDF, and 0.82 for the proposed consumption pattern. That is, the proposed consumption pattern analysis scheme can more accurately analyze consumption pattern because it uses both quantitative rating and qualitative reviews of consumers.

• 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.