• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.115-126
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• 2022

The purpose of this study is to try to IPA(Importance-Performance Analysis) by applying text mining approaches to user review data for korea mobile banking applications, and to derive priorities for improvement. User review data on mobile banking applications of korea commercial banks (Kookmin Bank, Shinhan Bank, Woori Bank, Hana Bank), local banks (Gyeongnam Bank, Busan Bank), and Internet banks (Kakao Bank, K-Bank, Toss) that gained from Google playstore were used. And LDA topic modeling, frequency analysis, and sentiment analysis were used to derive key attributes and measure the importance and satisfaction of each attribute. Result, although 'Authorizing service', 'Improvement of Function', 'Login', 'Speed/Connectivity', 'System/Update' and 'Banking Service' are relatively important attributes when users use mobile banking applications, their satisfaction is not at the average level, indicating that improvement is urgent.

• Cartoon and Animation Studies
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• s.15
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• pp.221-235
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• 2009

Most of the sounds used in animated films are artificially made. A large number of the sounds used are either actual sound recordings or diversely processed artificial sounds made with professional sound equipments such as synthesizers. One animation episode contains numerous amounts of sounds, resulting in significant sound production costs. These sounds have full potential to be reused in different films or animations, but in reality we fail to do so. This thesis discusses ways these sound sources can be acknowledged as added new values to the present market situation as a usable 'digital content'. The iTunes Music Store is an American Apple company product that is acknowledged as the most successful digital content distribution model at the time being. Its system's sound library has potential for application in the Korean sound industry. In result, this system allows the sound creator to connect directly to the online store and become the initiative content supplier. At the same time, the user can receive a needed content easily at a low price. The most important part in the construction of this system is the search engine, which allows users to search for data in short periods of time. The search engine will have to be made in a new manner that takes into consideration the characteristics of the Korean language. This thesis presents a device incorporating the Wiki System to allow users to search and build their own data bases to share with other users. Using this system as a base, the Korean animation sound library will provide development and growth in the sound source industry as a new digital sound content.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.397-404
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• 2021

Product image location in search engine advertising plays an important role in consumer perception when the product is relatively low involved and has functional value. The purpose of this research is to investigate the interaction effects of product image location and product type on advertising effectiveness. Building on the literature of location effects, we show that for products for which heaviness is considered a positive attribute, product image placed on the right are preferred. To test hypotheses, a 2(product image location: left vs. right) × 2(product type: single vs. bundle) experiment is conducted and a total of 144 paricipants took part in the experiment. The results revealed that respondents show higher brand attitude and purchse intention toward a bundle product's advertising with product image place on the right. The results provide implications and suggestions for improving search engine advertising and marketing strategies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.39-46
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• 2021

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6133-6145
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• 2013

With the increasing number of online wine stores, customers are increasingly seeking to purchase wine online. On the other hand, purchasing wine online is prohibited by law or regulation in Korea. Therefore, customers mainly search for wine information, inquire about wine products, and make a pre-purchase at an online wine store. Online wine stores play important roles in customer's purchase decision-making, and are likely to be a useful wine distribution channel in the near future. Therefore, the aim of this study was to identify the determinants of the online wine purchase intention, and examine the structural relationships between the determinants and online wine purchase intention. The site quality of online wine stores (information quality, system quality, service quality), and perceived value (quality value, price value, emotional value, social value) were selected as the determinants of online wine purchase intention based on literature review. The data was collected from those who had experience using an online wine store to purchase wine, and the data was used to test the proposed research model. The findings showed that the information quality was not related to the perceived value (quality value, price value, emotional value, social value). The system quality was proven to be positively and significantly related to the quality value, price value, and emotional value, whereas it had no impact on the social value. In addition, the service quality was found to affect the perceived value (quality value, price value, emotional and social value). Finally, the results showed that the quality value, emotional value, and social value have a positive impact on the online wine purchase intention, whereas the price quality is not related to the online wine purchase intention. These results are expected to make a contribution to a better understanding of how the quality of online wine stores and the customer's perceived value affect the online wine purchasing intention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3389-3404
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• 2012

Based on the case study methods, the study analyzes emergence and ensuing typology of global ebook platforms such as Google eBook, Amazon Kindle, iBooks Store. Global ebook platforms show adaptation process responding to rapidly changing digital technological envirment and it's fitness landscape. The critical elements in its emerging process are the distinct selection criteria, the degree of resource abundance and the search process based on open innovation. Based on these critical elements, the global platforms show speciation process, so called niche creation and are evolving into a variety of the typology based on the initial condition of key resource which makes the platform emerge and grow. Each global ebook platforms is evolving into open platform, hybrid platform, closed platform. Google eBook has openness and extensibility due to a variety of devices based on Android and a direct involvement of actors. Amazon Kindle has developed from a online bookstore and into the hybrid platform which have not only closed quality but also openness with ebook devices and mobile network. iBooks Store has developed into the closed platform through the agency model based on competitive hardwares and closed quality with iphone and ipad.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.682-695
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• 2019

In recent years, using a connected platform, companies have built and implemented a mobile viral marketing strategy to attract new customers and to have long-term relationships with existing customers. The emergence of interactive Web 2.0 has led to an explosive increase in customer engagement, and practitioners have become interested in connected platform to build close relationships with their customers. However, the study on the effectiveness of various customer influx methods using the connected platform that companies utilize for an increase of customer participation is insufficient. Based on the theoretical study of Sashi (2012), this study analyzes the actual mobile viral promotion of company A's open market shopping mall for the purpose of bringing new customers and having a long-term relationship with the new customers[1]. By analyzing the customer engagement type, the implications for the effectiveness of mobile viral promotion are suggested. First, as a result of the immediate effect of online viral promotion, promotions are partially effective in attracting new customers. Second, as a result of examining the change of customer engagement type in order to find out the long - term effect of online viral promotion, it was found that in most cases, new customers were not become satisfied customers and, Laggard Effect, which takes time to become a satisfied customer, has been confirmed.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.