• Journal of Information Technology Applications and Management
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• v.30 no.2
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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.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews 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.

• Journal of Plant Biotechnology
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• v.45 no.1
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• pp.9-16
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• 2018

The advent of available DNA barcoding technology has been extensively adopted to assist in the reference to differentiate the origin of various medicinal plants species. However, this technology is still far behind the curve of technological advances to be applied in a practical manner in the market to authenticate the counterfeit components or detect the contamination in the admixtures of medicinal plant species. Recently, a high resolution melting curve analysis technique was combined with the procedure of DNA barcoding (Bar-HRM) to accomplish this purpose. In this review, we tried to summarize the current development and bottleneck of processing related to the Bar-HRM technology for the practical application of medicinal plant species' differentiation in a viable global market. Although several successful results have been reported, there are still many obstacles to be resolved, such as limited number of DNA barcodes and single nucleotide polymorphisms, in particular, only one DNA barcode, internal transcribed sequence (ITS) of ribosomal DNA has been reported in the available nuclear genome. In addition, too few cases have been reported about the identification of counterfeit or contamination with processed medicinal plant products, in particular specifically the case of technology based infusion, jam and jelly products and components in which it is noted that DNA can be thereby degraded during the processing of these products and components.

• Archives of design research
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• v.19 no.4 s.66
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• pp.71-80
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• 2006

Collaboration has become essential in the product design process due to internationalized and specialized business environments. This study presents a real-time collaborative 3D design workspace for distributed designers, focusing on the development and the evaluation of new interaction techniques supporting nonverbal communication such as awareness of participants, shared manipulation and tele-presence. Requirements were identified in terms of shared objects, shared workspaces and awareness through literature reviews and an observational study. An Augmented Reality based collaborative design workspace was developed, in which two main interaction techniques, Turn-table and Virtual Shadow, were incorporated to support shared manipulation and tele-presence. Turn-table provides intuitive shared manipulation of 3D models and physical cues for awareness of remote participants. Virtual shadow supports natural and continuous awareness of location, gestures and pointing of partners. A lab-based evaluation was conducted and the results showed that interaction techniques effectively supported awareness of general pointing and facilitated discussion in 3D model reviews. The workspace and the interaction techniques can facilitate more natural communication and increase the efficiency of collaboration on virtual 3D models between distributed participants (designer-designer, engineer, or modeler) in collaborative design environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7485-7493
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• 2015

With the popularity of Korean Wave, making cultural goods specific for Hallyu tourists is getting more important. However, there are mainly daily life goods using celebrity character-based ones. Remarkably, there are only a few cultural goods especially in practicality-based clothing category. In particular, few cultural goods related to children's wear have been developed. Therefore, if children's wear is developed as Korean Wave cultural goods considering Chinese consumers' pattern and Korean Wave cultural goods, it will be helpful for revitalizing the Korean Wave and Korea's fashion market. In this regard, the purpose of this study is to develop children's wear design as Korean Wave cultural goods, thereby presenting empirical research results and fulfilling its following objectives: First, it is to identify the concept of Korean Wave cultural goods, to analyze the current status to finally establish data to develop Korean Wave cultural goods needed at this time. Second, it is to make real-life size works through development of designs to provide the empirical data for Korean Wave cultural goods market. For the research method and contents the review of the previous research, in-depth interview for qualitative research, and empirical research using market research and development of work were performed. Through the final research outcomes, Korean Wave cultural goods, the children's wear that can meet the consumer's needs were presented as empirical data. The study can be used as basic data for domestic fashion market and cultural product market and it is meaningful as a reference for the analysis on the Chinese consumers' needs.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.131-138
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• 2021

We reviewed current research trends for discriminating between species of the Angelica genus, a group of important medicinal plants registered in South Korea, China, and Japan. Since the registered species for medicinal purposes differ by country, they are often adulterated as well as mixed in commercial markets. Several DNA technologies have been applied to distinguish between species. However, one of the restrictions is insufficient single-nucleotide polymorphisms (SNPs) within the target DNA fragments; in particular, among closely-related species. Recently, amplification refractory mutation system (ARMS)-PCR and highresolution melting (HRM) curve analysis techniques have been developed to solve such a problem. We applied both technologies, and found they were able to discriminate several lines of Angelica genus, including A. gigas Nakai, A. gigas Jiri, A. sinensis, A. acutiloba Kitag, and Levisticum officinale. Furthermore, although the ITS region differs only by one SNP between A. gigas Nakai and A. gigas Jiri, both HRM and ARMS-PCR techniques were powerful enough to discriminate between them. Since both A. gigas Nakai and A. gigas Jiri are native species to South Korea and are very closely related, they are difficult to discriminate by their morphological characteristics. For practical applications of these technologies, further research is necessary with various materials, such as dried or processed materials (jam, jelly, juice, medicinal decoctions, etc.) in commercial markets.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.361-372
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• 2023

In this study, three luxury fashion start-up platforms, Balaan, Trenbe, and Must-it, were selected as research subjects. The purpose of this study is to compare and analyze the marketing mix strategies of each of the three online sites. The results of our study are as follows. First of all, the product strategies of the three luxury platform companies are characterized by the composition of products from high-end brands to SPA brands, and product composition such as kids, home living, Used goods and art in addition to women's and men's wear. In addition, the pricing strategies of luxury platforms show price differences depending on the luxury platform even for the same product. It is shown as a structure that directly determines margin. Therefore, in order to secure an edge in price competitiveness, each platform provided discount coupons and savings that are not available in offline stores such as department stores, providing opportunities to purchase luxury goods at a lower price than offline stores.Lastly, the sales promotion strategies of the three luxury platform companies was used include price discount promotions such as price discounts, discount coupons, and regular sales, and value-added sales such as membership registration/review points, events, product information, delivery services, social contribution activities, and SNS utilization.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.29-45
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• 2015

Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.