• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.81-89
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• 2020

Online reviews we encounter commonly on SNS, although a complex range of assessment information affecting the consumer's preferences are included, it is general that such information is just provided by simple numbers or star ratings. Based on those review types, it is not easy to get specific information that consumers want and use it to make a decision for purchase. Therefore, in this study, we propose a prediction methodology that can provide ratings broken down by evaluation items by performing sentiment analysis on restaurant reviews written in Korean. To this end, we select 'food', 'price', 'service', and 'atmosphere' as the main evaluation items of restaurants, and build a new sentiment dictionary for each evaluation item. It also classifies review sentences by rating item, predicts granular ratings through sentiment analysis, and provides additional information that consumers can use to make decisions. Finally, using MAE and RMSE as evaluation indicators it shows that the rating prediction accuracy of the proposed methodology has been improved than previous studies and presents the use case of proposed methodology.

• Journal of Korean Library and Information Science Society
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• v.52 no.2
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• pp.65-84
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• 2021

This study aimed to analyze the overall operation status of the Bank of Korea library representing the economic library in Korea, investigate user satisfaction and demand, and to propose a direction for the development of the economic library in the future. To this end, a case study was conducted on changes in the external environment of major libraries at home and abroad, and a user survey was conducted. As a result, the future tasks include introducing smart systems, robotics some library services, non-face-to-face service response spaces that reflect the times, introducing big data analysis services, strengthening user-tailored systems, and activating SNS communication. It proposed developing specialized libraries, online reference and research support services, and providing original DBs for publications to strengthen expertise, maintaining and expanding cloud services, maintaining and expanding cooperative projects, and collecting national knowledge and information.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.357-365
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• 2024

This study aims to provide essential foundational data for strengthening the primary roles and functions of Support Centers for Childcare (SCC) by analyzing the project status of promotional and information dissemination activities conducted over the past five years (2019-2023). The study results indicate that, in terms of promotion, the most common activity was the production of commemorative items, with text messaging being the primary promotional method. Promotional materials were predominantly distributed directly within the centers. Secondly, regarding information dissemination, job information was the most frequently provided content on the nationwide centers' websites, KakaoStory was the most utilized SNS platform, and newsletters were the most commonly produced and distributed publication form. Based on these findings, if the Support Centers for Childcare (SCC) efficiently manage their promotional and information dissemination strategies, they will be able to provide customized childcare support services that cater to the specific characteristics of each region in the future.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.313-321
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• 2014

In current society, the digital era that makes enormous amount of data, and the diversified city, the smart space, which has characteristics of creating, collecting and representing data, is appeared. After 2012, in the social media environment called hyper-connected society with wide-spread smart phone, people started to get interested in public data and big data by generalized mobile device and SNS. At first, development of forming platform of data was focused, but now, many different idea from diverse area have been suggested about data analysis and usage to visualize the space intellectualization service. To focus on the visualization process to increase the usage of this public data for ordinary people more than specialized people, this research grasps the present condition of open data and public data service from the current public data portal and considers the applicability of them. As the result of research, the analysis and application of data to ordinary people decrease the use of paper documents, and this research will help to develop the application which is fast and accurate about individual behavior and demand to utilize public data service in intellectual space.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.161-176
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• 2014

Social commerce is a new internet business model which are types of joint purchase, social link, promotion, and on-off association etc. The recent public organization has the dual target to increase sound finance, and to improve customer satisfaction and the quality of public service. The purpose of this study is to propose the strategy of public social commerce which makes it's customers become positive purchasers. We analyzed 31 public organizations, and found that the type of social link interlocking with SNS site in the various purpose of public, and the offline association type using the service of location base would be utilized very highly. We also found that the joint purchase type and the promotion type would be used in the area of public and private selective services intending to make a profit. The anticipated performance are as follows: rapidity and reliance, customer made thing and goodwill, convenience for the service environment quality, publicity and diffusion, and sales promotion, productivity increase and new finding of revenue model for the profit increase outcome.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.90-98
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• 2017

Since 2010, SNS was holding the explosive spread of smartphones has created a place of public advertising platform, and it entered the Internet of Things (IoT) is born gradually countdown of the era came to us already. In utilizing a variety of location-based services IoT services (beacon, O2O) The focus of this paper to analyze the differences in satisfaction with the oil layer by experienced users of what is being used. We consider the type and overall utilization of the concepts typical IoT service in the current service is made to expand the contents of the paper. Hypothesis was reconstructed ease, attractiveness, a survey reliability, value four kinds of models, called Honeycomb UX, User Experience Honeycomp Peter Mobil. Company that provides the service IoT in this study are expected to be used as basic data to help provide a more accurate personalized service according to the user's satisfaction difference.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.599-606
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• 2017

'Video Commerce' has grown significantly, and is in the era of so-called V-commerce 2.0. Based on this background, this study focused on the link and the possibility of creating synergy between V-commerce 2.0 content and MCN, and examined the linkage strategy considering its characteristics. In conclusion, first, V-Commerce has evolved into the age of 2.0, centered on the characteristics of content that are oriented towards fun and sympathy, beyond the 1.0 era. Second, V-commerce 2.0 content has the characteristic of replacing the sharing and recommendation based on the nature of SNS networks as promotion and purchase enhancement. Therefore, competitiveness as 'content' is relatively important before 'commerce'. Third, V-commerce 2.0 and MCN industry have a strong connection with each other in terms of securing core competitiveness and creating a new profit model. In order to create the synergy between V-Commerce 2.0 and MCN, we proposed the use of big data to reinforce V-Commerce 2.0 customized content competitiveness, building of storytelling marketing and branding, and enhancement of live performance and interactive communication.