• The Optical Journal
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• s.133
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• pp.73-79
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• 2011

올해로 20회째를 맞이하며 아시아 최고의 사진영상분야 전시회로 성장한 '2011 서울국제사진영상기자재전(이하 P&I 2011)'이 지난 4월 21일부터 24일까지 나흘간 서울 코엑스 본관 1층 A홀에서 성황리에 치러졌다. 통합 이미징 전시회를 지향하는 P&I 2011는 촬영 단계부터 편집, 출력, 저장, 활용에 이르기까지 사진 전반의 것을 아우르는 전시회로 거듭나는 한편, 타 분야와의 융 복합을 통해 카메라 본연의 핵심기술을 활용한 혁신적인 제품이 선보였다. 국내 토종카메라 업체인 삼성을 비롯하여 캐논, 니콘, 올림푸스, 파나소닉 등 카메라 제조사와 프린터 및 사진 액세서리 업체 등 세계 20개국 117개사가 참가했으며 전시기간동안 전년도보다 105.4%나 늘어난 총 7만700여명의 참관객이 다녀간 것으로 나타나 카메라를 비롯한 사진영상분야에 대한 소비자들의 뜨거운 관심을 재확인할 수 있었다.

• Distribution Business Review
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• no.3
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• pp.105-119
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• 2003

인터넷 전자상거래와 TV홈쇼핑 상품 구매가 활성화됨에 따라 인터넷 쇼핑몰과 TV홈쇼핑의 소비자 행동특정을 비교 분석하여 두 집단간에 통계적으로 유의한 차이자 있는지 분석하고 이에 대한 마케팅 시사점을 제시하고자 한다. 교차분석 및 T-검정 등을 활용한 통계분석 결과 인터넷 상품구매와 TV 홈쇼핑을 각각 선호하는 두 집단간에 정보수집 등 사전준비 요인, 제품에 대한 편리성 및 서비스 요인 인터넷 사용 환경 풍의 요인에서 두 집단간에 통계적으로 유의적인 차이가 존재하는 것으로 밝혀졌다. 인터넷 전자상거래나 TV 홈쇼핑 업체들은 두 집단간의 이러한 차이 특성을 충분히 고려하여 인터넷 또는 TV 홈쇼핑 마케팅 전략을 운영하여야 할 것이다. 특히 소비자들의 통신판매 제품에 대한 관심이 높아져가 것을 감안하여 대 고객 관계마케팅(CRM)시스템 부문 강화, '상품배송' 면에서 비용 절감과 고객 만족을 위한 SCM 구축방안 개발에 주력하여야 할 것이다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.586-590
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• 2023

인터넷과 소셜미디어 사용량의 급증으로, 제품 리뷰, 온라인 피드백, 소셜 미디어 게시물 등을 통해 고객의 감정을 파악하는 것이 중요해졌다. 인공지능이 활용되어 고객이 제품이나 서비스의 어떤 부분에 만족하거나 불만을 가지는지를 분석하는 연구를 ABSA라고 하며 이미 해외에서는 이런 연구가 활발하게 이루어지는 반면, 국내에서는 상대적으로 부족한 상황이다. 이 연구에서는 ABSA의 두 개의 주요 작업인 ACD와 ASC에 대해 생성 모델 중 하나인 T5 모델을 사용하는 방법론을 제시한다. 이 방법론은 기존 판별 모델을 사용하는 것에 비해 시간과 성능 측면에서 크게 향상되었음을 보여준다.

• Korean Journal of Human Ecology
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• v.21 no.5
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• pp.1005-1016
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• 2012

Recently, as growing number of consumers publish product and service reviews on the Internet, e-review has received attention from retailers and researchers. E-review, a form of electronic word-of-mouth (eWOM) which is typically shared between strangers whose identity and credibility are unknown, has become an important product information source as social media has facilitated information exchanges between more consumers. The objective of this study was to investigate the effects of e-review attributes on purchase intention for fashion products, which is mediated by trust of e-review, as well as to explore the differences between consumer communities and cooperative communities. A questionnaire was developed based on previous researches. Data were gathered from adults living in Busan. The results were analyzed by factor analysis, t-test, and regression using SPSS 18.0. The results showed that consumers tended to recognize e-reviews from consumer communities as exaggerated information, while they considered reviews from cooperative communities as reliable information, which gave the latter higher purchase intention. There were significant differences in e-review attributes for fashion products (e.g., Exaggeration, Entertainment, Innocence, and Agreement), purchase intention between consumer communities (e.g: Blog, Internet cafe) and cooperative communities (e.g: general malls and specialty malls). For both communities, purchase intention of fashion products was influenced by its entertainment attributes and perceived trust of e-reviews. These results suggest that e-retailers need to focus on understanding the causes of purchase intention with e-reviews for fashion products. Specifically, e-retailers should recognize that e-reviews of fashion products were associated primarily with entertaining and with consumers' trust. Based on these findings, managerial implications are presented.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.69-90
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• 2023

The purpose of this study is to evaluate the service attributes and consumer reactions of telemedicine apps in South Korea and visualize their differentiation by constructing positioning maps. We crawled 23,219 user reviews of 6 major telemedicine apps in Korea from the Google Play store. Topics were derived by BERTopic modeling, and sentiment scores for each topic were calculated through KoBERT sentiment analysis. As a result, five service characteristics in the application attribute category and three in the medical service category were derived. Based on this, a two-dimensional positioning map was constructed through principal component analysis. This study proposes an objective service evaluation method based on text mining, which has implications. In sum, this study combines empirical statistical methods and text mining techniques based on user review texts of telemedicine apps. It presents a system of service attribute elicitation, sentiment analysis, and product positioning. This can serve as an effective way to objectively diagnose the service quality and consumer responses of telemedicine applications.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Korean journal of applied entomology
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• v.62 no.3
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• pp.193-205
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• 2023

Botanical insecticides derived from plant extracts exhibit repellent, antifeedant and enzyme-inhibiting activities against insect pests. Among such pests, phytophagous mites are major threats to horticultural crops. Botanical extracts derived from neem, chrysanthemum, and shrubby sophora are employed as field acaricides. These botanical extracts have low toxicities against natural enemies of the insect pests and, thus, are valuable in pest management. This review focuses on the potential for botanical extracts in the controls of mites, with comparisons of the spectrum of activity, the lethal dose and times and their mode of action. This information will enable better formulation of botanical extracts in integrated mite control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.