• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.914-915
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• 2017

Importance and usage of the recommender system increases with the increase of information. The accuracy of the system recommendation primarily depends on the data. There is a problem in recommender systems, known as cold start problem. The lack of data about new products and users causes the cold start problem, and the system will not be able to give correct recommendation. This paper deals with cold start problem by comparing product specification and the review of the resembled products. The user, who likes the resembled product of the new one has more probability of taking interest in the new product as well. However, if a user disagreed with resembled product due to some reasons which the user mentioned in the reviews. The new product overcomes that issue, so the user will greatly accept the new product. Therefore, the system needs to recommend new product to those users as well, in this way the cold start problem will get resolved.

• 품질경영학회지
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• 제51권4호
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• pp.701-713
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• 2023

Purpose: The primary purpose of this study is to integrate text mining and Quality Function Deployment (QFD) to automatically extract valuable information from customer reviews, thereby establishing a QFD frame- work to confirm genuine customer needs for New Product Development (NPD). Methods: Our approach combines text mining and QFD through topic modeling and sentiment analysis on a large data set of 56,873 customer reviews from Zappos.com, spanning five running shoe brands. This process objectively identifies customer requirements, establishes priorities, and assesses competitive strengths. Results: Through the analysis of customer reviews, the study successfully extracts customer requirements and translates customer experience insights and emotions into quantifiable indicators of competitiveness. Conclusion: The findings obtained from this research offer essential design guidance for new product develop- ment endeavors. Importantly, the significance of these results extends beyond the running shoe industry, presenting broad and promising applications across diverse sectors.

• Journal of Information Technology Applications and Management
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• 제30권2호
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• 패션비즈니스
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• 제22권3호
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• pp.89-108
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• 2018

This paper aims to explore the analysis of the meanings and processes of reading online consumer reviews and to construct a substantive theory that explains the process involved with the phenomenon of reading consumer reviews. In order to explore the phenomenon, this study employs a qualitative methodology. Following the grounded theory perspective, the researcher conducted interviews with 17 participants, who have subsequently shopped online and utilized online consumer reviews for shopping, and decidedly employed in-depth interviews with those participants. Through coding and making constant comparison, several themes emerged: improving confidence, trusting reviews, getting a sense of who reviewers are, seeking balance, processing and handling negative reviews, experiencing vicariously, increasing searchability, getting a sense of who they are in terms of similarity, and seeking benefits and the usage situations from consumer based reviews. Among the emerging themes, improving confidence can be considered a core category, which is influenced by the analysis of trusting reviews and the consumer vicarious experiences with a product. Moreover, this study discusses the relationships among the themes. This study concludes with a discussion of the results, implications, and limitations.

• 한국IT서비스학회지
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• 제11권3호
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• pp.103-127
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• 2012

With the advent of SNS (Social Network Services), the product reviews by friends in SNS are intensively utilized for online marketing. However, there is a lack of empirical evidence on the actual marketing effect of SNS reviews, although we need to identify who can be the target of SNS marketing in terms of customer attributes, preferences, or experiences. In this study, we investigate the moderating role of customer attributes in identifying the effect of SNS reviews on customer purchasing decision. As the moderating variables, we adopt 'information search experience' and 'perception of information overload'. Research results evidence that, in order to understand the effect of SNS reviews in a comprehensive manner, we need to examine it in the context of various related factors such as 'information search experience' and 'perception of information overload'. The results show that the persuading effect of SNS reviews for product purchasing is stronger for the customers with the lower information search experiences as well as the lower perception on the information overload on the web. This result delivers managerial implications on who can be the target customers of SNS marketing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5120-5142
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• 2018

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

• 한국의류학회지
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• 제35권7호
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• pp.761-774
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• 2011

This study investigated the consumer review information considered important by consumers when making a purchase decision to buy apparel products online. Data were collected through focus group interviews. Eleven females in their 20s and 30s, who have extensive experience in reading consumer reviews posted on online apparel stores, participated in the study. The consumer review information considered important by participants is the information related to seven product attributes (size, fabric, design, color, sewing, price, and country of origin), seven benefits (functional, financial, esthetic, emotional, social, utilitarian benefits, and product value compared to price) of the apparel product and four store attributes (return/refund, delivery, reputation/credibility, and customer service). The findings from the study can serve as an important tool in developing survey questions in order to evaluate the quality of consumer review information and help online retailers plan methods to improve the quality of reviews.

• 유통과학연구
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• 제19권4호
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• pp.25-35
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• 2021

Purpose: This study aims to analyze the factors affecting customer satisfaction in the customer reviews of omni-channel, posted on Internet blogs, cafes, and YouTube using text mining analysis. Research, data, and Methodology: In this study, frequency analysis is performed and the LDA (Latent Dirichlet Allocation) is used to analyze social big data to respond to reviewers' reaction to the recently opened omni-channel shopping reviews by L Shopping Company. Additionally, based on the topic analysis, we conduct a sentiment analysis on purchase reviews and analyze the characteristics of each topic on the positive or negative sentiments of omni-channel app users. Results: As a result of a topic analysis, four main topics are derived: delivery and events, economic value, recommendations and convenience, and product quality and brand awareness. The emotional analysis reveals that the reviewers have many positive evaluations for price policy and product promotion, but negative evaluations for app use, delivery, and product quality. Conclusions: Retailers can establish customized marketing strategies by identifying the customer's major interests through text mining analysis. Additionally, the analysis of sentiment by subject becomes an important indicator for developing products and services that customers want by identifying areas that satisfy customers and areas that evoke negative reactions.

• 지능정보연구
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• 제26권2호
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• pp.57-78
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• 2020

인터넷의 발달로, 소비자들은 이커머스에서 손쉽게 상품 정보를 확인한다. 이때 활용되는 상품 리뷰는 사용자 경험을 토대로 작성되어 구매의사결정의 효율성을 높일 뿐만 아니라 상품 개발에 도움을 주기도 한다. 하지만, 방대한 양의 상품 리뷰에서 관심있는 평가차원의 세부내용을 파악하는 데에는 많은 시간과 노력이 소비된다. 예를 들어, 노트북을 구매하려는 소비자들은 성능, 무게, 디자인과 같은 평가차원에 대해 각 차원별로 비교 상품의 평가를 확인하고자 한다. 따라서 본 논문에서는 상품 리뷰에서 다차원 상품평가 점수를 자동적으로 생성하는 방안을 제안하고자 한다. 본 연구에서 제시하는 방안은 크게 2단계로 구성된다. 사전준비 단계와 개별상품평가 단계로, 대분류 상품군 리뷰를 토대로 사전에 생성된 차원분류모델과 감성분석모델이 개별상품의 리뷰를 분석하게 된다. 차원분류모델은 워드임베딩과 연관분석을 결합함으로써 기존 연구에서 차원과 단어들의 관련성을 찾기 위한 워드임베딩 방식이 문장 내 단어의 위치만을 본다는 한계를 보완한다. 감성분석모델은 정확한 극성 판단을 위해 구(phrase) 단위로 긍부정이 태깅된 학습데이터를 구성하여 CNN 모델을 생성한다. 이를 통해, 개별상품평가 단계에서는 구 단위의 리뷰에 준비된 모델들을 적용하고 평가차원별로 종합함으로써 다차원 평가점수를 얻을 수 있다. 본 논문의 실험에서는 대분류 상품군 리뷰 약 260,000건으로 평가모델을 구성하고, S사와 L사의 노트북 리뷰 각 1,011건과 1,062건을 실험데이터로 활용한다. 차원분류모델은 구로 분해한 개별상품 리뷰를 6개 평가차원으로 분류했고, 기존 워드임베딩 방식보다 연관분석을 결합한 모델의 정확도가 13.7% 증가했음을 볼 수 있었다. 감성분석모델은 문장보다 구 단위로 학습한 모델이 평가차원을 면밀히 분석함으로써 29.4% 더 높은 정확도를 보임을 확인했다. 본 연구를 통해 판매자, 소비자 모두가 상품의 다차원적 비교가 가능하다는 점에서 구매 및 상품 개발에 효율적인 의사결정을 기대할 수 있다.

• 한국IT서비스학회지
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• 제14권4호
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.