• 한국감성과학회:학술대회논문집
• /
• 한국감성과학회 2011년도 춘계학술대회
• /
• pp.49-50
• /
• 2011

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

• 한국가정과교육학회지
• /
• 제22권3호
• /
• pp.129-145
• /
• 2010

서울지역 고등학생을 대상으로 인터넷 쇼핑몰에서의 패션제품 구매행동, 구매후기에 대한 인식, 구매후기의 이용과 작성 및 그에 따른 인식, 의복관여도에 따른 구매후기에 대한 인식 및 구매 후 불만경험과 대응행동을 살펴보았다. 서울지역의 6개 고등학교 508명의 학생들을 대상으로 설문조사를 실시하였고 자료분석은 SPSS 17.0을 이용하여 평균, 표준편차, 빈도, t-test, 일원분산분석을 하였으며 사후검정으로는 Duncan's Multiple Test를 실시하였다. 인터넷 쇼핑몰에서 패션제품 구매이유는 저렴한 가격과 다양성 및 편리성 때문이였고 구매하지 않는 이유는 대부분 화면과 실제의 상품 차이 때문이었다. 학생들은 구매후기를 믿을만하고 유용하다고 인식하였다. 구매후가 내용의 방향과 개수에 대해서는 영향을 받았지만 최신성에 대해서는 영향 받지 않는 것으로 나타났다. 구매후기를 이용하는 학생이 이용하지 않는 학생보다 유용성, 신뢰도, 영향력 모두 높게 인식하였고, 구매후기를 작성하는 학생들이 작성하지 않는 학생들보다 구매후기의 유용성과 신뢰도, 구매후기의 개수에 따른 영향력을 높게 인식하였다. 의복관여도에 따라서는 고관여의 학생들이 중관여와 저관여의 학생들에 비해 구매후기를 유용하다고 인식하였다. 인터넷 쇼핑몰을 통해 패션제품을 구매 후 불만을 경험한 학생들은 불평행동으로 공행동과 무행동을 주로 하는 것으로 나타났다.

• Asia pacific journal of information systems
• /
• 제25권4호
• /
• pp.763-783
• /
• 2015

Online customer reviews (i.e., electronic word-of-mouth) has gained considerable interest over the past years. However, a knowledge gap exists in explaining the mechanisms among the factors that determine the product sales in online retailing environment. To fill the gap, this study adopts a principal-agent perspective to investigate the effect of customer reviews and customer incentives on product sales in online retail stores. Two customer review factors (i.e., average review ratings and the number of reviews) and two customer incentive factors (i.e., price discounts and special shipping offers) are used to predict product sales in regression analysis. The sales ranking data collected from the video game titles at Amazon.com are used to analyze the direct effects of the four factors and the interaction effects between customer review and customer incentive factors to product sales. Result reveals that most relationships exist as hypothesized. The findings support both the direct and interaction effects of customer reviews and incentive factors on product sales. Based on the findings, discussions are provided with regard to the academic and practical contributions.

• ETRI Journal
• /
• 제33권5호
• /
• pp.720-730
• /
• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• 한국IT서비스학회지
• /
• 제19권4호
• /
• pp.85-107
• /
• 2020

The Internet of Things market, a technology that connects the Internet to various things, is growing day by day. Besides, various smart home services using IoT and AI (Artificial Intelligence) are being launched in homes. Related to this, existing smart home-related studies focus primarily on ICT technology, not on what service improvements should be made in customer positions. In this study, we will use smart home application customer review data to classify functional and design elements of smart home usability value and examine the ways customers think of service improvement. For this, LG Electronics and Samsung Electronics" Smart Home application, the main provider of Smart Home in Korea, customer reviews were crawled to conduct a comparative analysis between them. In this study, the review of IoT home-applications was analyzed to find service improvement insights from customer perspective, and related analysis of text mining, social network analysis and Doc2vec was used to efficiently analyze data equivalent to about 16,000 user reviews. Through this research, we hope that related companies effectively seek ways to improve smart home services that reflect customer needs and are expected to help them establish competitive strategies by identifying weaknesses and strengths among competitors.

• 품질경영학회지
• /
• 제30권4호
• /
• pp.120-136
• /
• 2002

The CRM is the process of integrated customer management to increase the profitability of firm as a maximizing the consumer's value and supplying the high quality product or service. The Post Information Service Headquarter was recognizing the importance of CRM and constructed the customer relationship management system that based on CRM, steadily has made an endeavor for operating to improve the job operation such as posting, banking and insurance. This research analysed the impact of service quality on the customer satisfaction in the Korea Post Office. First of all, we review the existing literature on measurement of service quality and management. As a result of this review and survey of the employer in post office, nineteen factors emerged as important to the service management of The Korea Post Office; Postal Services, Banking Services, Insurance Services. The regression analysis was utilized for analyzing the influence of service quality factors upon the customer satisfaction. Results show that service quality factors have a statistically significant impact on the customer satisfaction of the Korea Post Office.

• 한국인공지능학회지
• /
• 제12권2호
• /
• pp.25-33
• /
• 2024

This study examines the relationship between customer satisfaction and management response in the hotel industry amid the COVID-19 pandemic. By applying regression analysis and topic modeling to consumer reviews on online platforms, we assess how consumer perceptions and management behaviors have shifted since the onset of the pandemic. The findings reveal a significant decline in customer satisfaction linked to COVID-19. Significantly, while the pandemic has reduced overall customer satisfaction levels, high response rates and high review-response content similarity mitigate the impact of the crises. These results highlight the critical need for hotel managers to continuously monitor online reviews and adapt their engagement strategies to maintain and enhance customer satisfaction during ongoing and future crises. This research not only corroborates existing theories on customer satisfaction but also exposes novel dynamics introduced by the pandemic, offering new insights for effective customer relationship management in turbulent times.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권8호
• /
• pp.2137-2156
• /
• 2024

Customer reviews are the second-most reliable source of information, followed by family and friend referrals. However, there are many existing customer reviews. Some online shopping platforms address this issue by ranking customer reviews according to their usefulness. However, we propose an alternative method to rank customer reviews, given that this system is easily manipulable. This study aims to create a ranking model for reviews based on their usefulness by combining product and seller service aspects from customer reviews. This methodology consists of six primary steps: data collection and preprocessing, aspect extraction and sentiment analysis, followed by constructing a regression model using random forest regression, and the review ranking process. The results demonstrate that the ranking model with service considerations outperformed the model without service considerations. This demonstrates the model's superiority in the three tests, which include a comparison of the regression results, the aggregate helpfulness ratio, and the matching score.

• 한국IT서비스학회지
• /
• 제17권3호
• /
• pp.1-16
• /
• 2018

There exist a number of studies pertaining to the determinants of customer satisfaction between low-cost and full-service carriers in the airline industry. Most studies measured service quality using SERVQUAL based on a survey method. This study offers a new perspective by employing a big data analytic approach using SNS data, which reflects the immediate response of customers as well as trends in real time. This study chose eight factors from TripAdvisor's customer review site as determinants of customer satisfaction and compared the differences between low-cost and full-service airlines. The factors analyzed were seat comfort, customer service, cleanliness, food and beverage, legroom, entertainment, value for money, and check-in and boarding. Additionally, ratings from domestic and foreign customers were compared. The findings show that customer service and value for money are significant factors in satisfaction with low-cost airlines while all variables except legroom and entertainment are significant for full-service airlines. The results show that SNS-based data and analysis of big data are important for improving decision-making effectiveness and increasing customer satisfaction in the airline industry.