• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.139-148
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• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.882-884
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• 2019

오늘날 정보화 시대에서는 온라인 쇼핑의 상품리뷰 등 대용량의 텍스트 문서가 존재하며 제품에 대한 정서적인 의견뿐만 아니라 제품 선호도 및 상품 비교와 같은 유용한 정보를 제공한다. 본 논문에서는 사용자가 작성한 상품 리뷰로부터 제품의 특성을 비교하는 비교의견을 추출하기 위해 적용한 다양한 텍스트 마이닝 기법의 비교 결과를 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.36-48
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• 2020

App market has continuously been growth since its launch. The market revenues will reach about 1,000 billion US dollars in 2019. App is a core service for smartphone. Currently, there are more than 1.5 million mobile apps in App platform calling out for attention. So, if you are looking at developing a successful app, you need to have a solid marketing and distribution strategy. Online word of mouth(eWOM) is one of the most effective, powerful App marketing method. eWOM affect potential consumers' decision making, and this effect can spread rapidly through online social network. Despite the increasing research on word of mouth, only few studies have focused on content analysis. Most of studies focused on the causes and acceptance of eWOM and eWOM performance measurement. This study aims to content analysis of mobile apps review In 2013, Google researchers announced Word2Vec. This method has overcome the weakness of previous studies. This is faster and more accurate than traditional methods. This study found out the relationship between mobile app reviews and checked for reactions by Word2vec.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1204-1205
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• 2023

본 논문은 소비자의 리뷰 데이터를 기반으로 한 새로운 감성 분석 방법을 제안한다. 긍정, 부정, 중립으로 분류하는 전통적 감성 분석방법은 텍스트에 나타난 감정의 섬세한 차이를 파악하기 어렵다. 이에 본 연구에서는 GPT 모델을 사용하여 텍스트에서 사용자의 감정을 8 가지의 카테고리로 세분화한다. 부정적 정서를 가진 리뷰에서 분노, 혐오, 실망과 같은 구체적인 감정들을 직관적으로 파악할 수 있었고, 감정의 강도까지 파악할 수 있었다. 제안된 방법을 통해 기업은 고객의 요구 사항을 정확하게 인지할 수 있으며, 고객 맞춤형 서비스 개선에 기여할 수 있다는 점이 기대된다.

• 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 Internet of Things and Convergence
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• v.8 no.5
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• pp.47-54
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• 2022

The development of the Internet of Things led to new services that did not exist before. This required a change to the existing network. This study aims to verify the service quality, satisfaction, repurchase intention relationship, and the moderating effect of online reviews of Chinese consumers using fashion shopping malls. The results of the study showed that from the perspective of consumers in their 20s and 30s in China, the type, reliability, convenience, and interaction of service quality had a positive effect on customer satisfaction and repurchase intention. In addition, negative reviews among online reviews had a great influence on repurchase intention. Based on the results of the study, it will help improve the effect on online product reviews and in-depth understanding of the acceptance of online product reviews for online fashion shopping malls, and establish strategies for fashion companies to effectively manage online product reviews information.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.75-86
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• 2020

Hotel review data can extract various information that includes purchasing factors that lead to consumption, advantages, and disadvantages for hotels. In particular, the sentiment keyword of the review data helps consumers understand the pros and cons of hotels. However, it is not efficient for consumers to read a large number of reviews. Therefore, it is necessary to offer a summary review to customers. In this study, we suggest providing summary information on sentiment keywords association as well as a network of hotels based on sentiment keywords. Based on a sentiment keyword dictionary, the extracted sentiment keywords associations construct the hotel network through topological data analysis based mapper. This hotel network allows a consumer to find some hotels associated with specific sentiment keywords as well as recommends the same related hotels. This summary information provides users with a summarized emotional assessment of hotels and helps hotel marketing teams understand consumers' perceptions of their hotel.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.1-10
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• 2021

This study analyzes the use and meaning of consumer personality implication words in the review text of the Used Transaction Application . From of May 2021, the data were collected for the past six months by our Web crawler in Seoul and Gyeonggi Province, and a total of 1368 cases were collected first by random sampling, and finally 570 cases were preprocessed. The results are as follows. First, 48.2% of review texts were related to the personality of consumers even though it was a commercial platform of products. Second, the review text is mainly positive, which formed a text network structure based on the keyword 'gratitude'. Third, the review text, which implies consumer character, was divided into two groups: 'extrovert personality' and 'introvert personality' of consumers. And the individuality of the two groups worked together on the platform. In conclusion, we would like to suggest that consumer personality plays an important role in the platform transaction process, that consumer personality will play a role in the services of the platform in the future, and that consumer personality should be studied from various perspectives.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.