• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.186-190
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• 2010

본 논문은 쇼핑몰의 이용 후기 중 광고성 리뷰를 분류해 내는 방법을 제안한다. 여기서 광고성 리뷰는 주로 업체에서 작성하는 것으로 리뷰 안에 광고 내용이 포함되어 있다. 국외 연구 중에는 드물게 오피니언 스팸 문서의 분류 연구가 진행되고 있지만 한국어 상품평으로부터 광고성 리뷰를 분류하는 연구는 아직 이루어지지 않고 있다. 본 논문에서는 Naive Bayes Classifier를 활용하여 광고성 리뷰를 분류하였다. 이때 확률 계산을 위해 사용된 특징 단어는 POS-Tagging+Bigram, POS-Tagging+Unigram, Bigram을 사용하여 추출하였다. 실험 결과는 POS-Tagging+Bigram 방법을 이용하였을 때 광고성 리뷰의 F-Measure가 80.35%로 정확도 높았다.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.58-68
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• 2019

Recently SNS product reviews are excessively increasing. However, many SNS reviews are under feeble regulation than how big and powerful that their awarenesses are. This problem leads to consumers' discontentment on product reviews on online. This study aims to analyze how SNS product reviews characteristics: informativeness, entertainment, reliability and familiarity attribute on consumers' purchase intent and brand attitude. However, at this time, consumers' high discontents (stored-knowledge) expect to have negative affect on product reviews thus I put this as a regulation effect. This study is consisted of 240 examinee who check SNS product reviews before buying products.

• Knowledge Management Research
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• v.21 no.3
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• pp.177-195
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• 2020

The social consciousness on fake reviews has triggered researchers to suggest ways to cope with them by analyzing contents of fake reviews or finding ways to discover them by means of structural characteristics of them. This research tried to collect data from blog posts in Naver and detect habitual patterns users use unconsciously by variables extracted from blogs and blog posts by a machine learning model and wanted to use the technique in predicting fake reviews. Data analysis showed that there was a very high relationship between the number of all the posts registered in the blog of the writer of the related writing and the date when it was registered. And, it was found that, as model to detect advertising reviews, Random Forest is the most suitable. If a review is predicted to be an advertising one by the model suggested in this research, it is very likely that it is fake review, and that it violates the guidelines on investigation into markings and advertising regarding recommendation and guarantee in the Law of Marking and Advertising. The fact that, instead of using analysis of morphemes in contents of writings, this research adopts behavior analysis of the writer, and, based on such an approach, collects characteristic data of blogs and blog posts not by manual works, but by automated system, and discerns whether a certain writing is advertising or not is expected to have positive effects on improving efficiency and effectiveness in detecting fake reviews.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.77-79
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• 2019

블록체인을 이용하여 리뷰의 무결성을 검증하고 리뷰 제공자에게 보상을 지급한다. 기존의 리뷰 시스템에서는 돈을 받고 광고해주거나 악의적인 목적을 가진 악성 리뷰가 많이 존재한다. 리뷰 제공자에 대한 적절한 보상이 없어 리뷰 제공자가 직접 광고 유치 등을 통해 수익을 창출해왔다. 이 리뷰 시스템을 통해 리뷰 제공자는 정당한 노력의 보상을 받을 수 있고 사용자들도 신뢰할 수 있는 정보를 제공 받을 수 있다. 이러한 시스템을 통해 선순환적인 리뷰 생태계를 구축하고자 한다.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.279-290
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• 2022

As the growth of the live streaming service market is accelerating due to COVID-19, the number of downloads and reviews of live streaming mobile applications is also rapidly skyrocketing. This study is to research game streaming applications using Twitch reviews as database. A total of 8 topics are extracted through LDA topic modeling and 7 out of them are detected to be inconvenience factors. Then, to pinpoint the main inconvenience factors, co-occurrence analysis is used in order to find out main factors. Finally, based on previous studies, several solutions are provided, which can solve the inconvenience factors(advertisement, UI design, technology problems) as well as strengthening digital competitiveness. This study will serve as an opportunity to improve digital competitiveness not only for Twitch but also for other game live streaming service companies in the future.

• Management & Information Systems Review
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• v.39 no.2
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• pp.181-193
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• 2020

This study is a study that analyzes consumer perception by utilizing text mining, which is a recent issue. we analyzed the consumer's perception of Samsung Galaxy by analyzing consumer reviews of Samsung Galaxy YouTube ads. for analysis, 1,819 consumer reviews of YouTube ads were extracted. through this data pre-processing, keywords for advertisements were classified and extracted into nouns, adjectives, and adverbs. after that, frequency analysis and emotional analysis were performed. Finally, clustering was performed through CONCOR. the summary of this study is as follows. the first most frequently mentioned words were Galaxy Note (n = 217), Good (n = 135), Pen (n = 40), and Function (n = 29). it can be judged through the advertisement that consumers "Galaxy Note", "Good", "Pen", and "Features" have good functional aspects for Samsung mobile phone products and positively recognize the Note Pen. in addition, the recognition of "Samsung Pay", "Innovation", "Design", and "iPhone" shows that Samsung's mobile phone is highly regarded for its innovative design and functional aspects of Samsung Pay. second, it is the result of sentiment analysis on YouTube advertising. As a result of emotional analysis, the ratio of emotional intensity was positive (75.95%) and higher than negative (24.05%). this means that consumers are positively aware of Samsung Galaxy mobile phones. As a result of the emotional keyword analysis, positive keywords were "good", "good", "innovative", "highest", "fast", "pretty", etc., negative keywords were "frightening", "I want to cry", "discomfort", "sorry", "no", etc. were extracted. the implication of this study is that most of the studies by quantitative analysis methods were considered when looking at the consumer perception study of existing advertisements. In this study, we deviated from quantitative research methods for advertising and attempted to analyze consumer perception through qualitative research. this is expected to have a great influence on future research, and I am sure that it will be a starting point for consumer awareness research through qualitative research.

• Korean Journal of Construction Engineering and Management
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• v.14 no.1
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• pp.52-62
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• 2013

Owners' efforts to acquire Green building certification, which started in 2002, have been rapidly increasing since 2006. The causes of the increased owners' interests are due to various incentives, legal obligation, and purpose of advertising. As project owners generally are deficient in capabilities and knowledge on how to deal with administrative works on certification, they tend to fulfill the minimum requirements for certification. However, effective administration process need capabilities of identifying individual making decision point and review information. The implementation of green building certification system is expected to provide comfort to both occupants and potential users. Furthermore it contributes to reducing energy costs throughout the phase of O&M. In addition, technology innovation in green industry can be obtained. Therefore, this study is intended to support owners in order that they can clarify certification tasks and make a rational decision-making in time. For this purpose, first of all, the major decision points were selected as the gateways of green building certification process. And then management system based on IDEF3 modeling was developed for supporting owners' decision-making performance. This management system will improve owners' overall capacity in handling all the tasks regarding the certification of Green building.