• 한국콘텐츠학회논문지
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• 제13권12호
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• pp.201-211
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• 2013

본 연구의 목적은 온라인 뉴스에 대한 베스트 댓글이 일반 댓글의 동조에 미치는 효과에 대해 검증하는 것이었다. 참가자는 총 194명이었으며, 참가자들은 통제집단(61명), 긍정적 베스트 댓글 집단(66명), 부정적 베스트 댓글 집단(67명) 중 한 집단에 무선할당 되었다. 참가자들은 온라인 뉴스 기사와 긍정적 또는 부정적인 베스트 댓글을 읽고 하단에 제시된 댓글 난에 자신의 의견을 댓글 형식으로 작성하였으며, 주제 관여도와 자기표현 정도를 묻는 설문지를 작성하였다. 참가자들이 직접 작성한 댓글은 4명의 연구자가 읽은 뒤 댓글 방향성을 긍정, 부정, 중립으로 분류하였으며, 평가자간 신뢰도는 평균 84.9%였다. 분석 결과, 실험 집단에 따른 주제 관여도와 자기표현 정도에는 유의미한 차이가 없어, 실험 집단 간 동질성이 확보되었다. 그리고 교차분석 결과, 실험 집단에 따라 긍정, 부정, 중립적 댓글 빈도가 유의미하게 차이가 있었다. 사후검증 결과, 통제집단과 긍정적 베스트 댓글 집단, 그리고 긍정적 베스트 댓글 집단과 부정적 베스트 댓글 집단 간 댓글 방향성에는 유의미하게 차이가 있었으나, 통제집단과 부정적 베스트 댓글 집단 간 댓글 경향에는 유의미한 차이가 없었다.

• 한국IT서비스학회지
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• 제14권2호
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• pp.177-193
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• 2015

A great number of customers, who want to watch movies usually check out online reviews before choosing what to watch a movie. The most representative online media that customers consult are portal sites and SNS (Social Network Service). Although there have been numerous studies on online eWOM (e-Word of Mouth) and the effects of online media in businesses, it remains a question that which media is best for WOM (Word of Mouth) when selecting movies. This research examines customer's intention for consulting eWOM and for watching movies according to the number and tendency of online replies. We have compared portal sites and SNS about information of movie. The study shows that a large number of positive replies can affect the intention for WOM and choosing movies. Facebook has more influence than portal sites when choosing what to watch when replies consist of large and positive comments. However, there is no difference between the two types of media when they consist of negative comments.

• International journal of advanced smart convergence
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• 제11권1호
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• pp.11-18
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• 2022

Securities and investment services have and use large data. Investors started to invest through their own analysis methods. There are 22 major securities and investment companies in Korea and only 6 companies support open API. Python is effective for requesting and receiving, analyzing text data from open API. Daishin Securities Co. is the only open API that officially supports Python, and eBest Investment & Securities Co. unofficially supports Python. There are two important differences between CYBOS plus of Daishin Securities Co. and xingAPI of eBest Investment & Securities Co. First, we must log in to CYBOS plus to access the server of Daishin Securities Co. And the python program does not require a logon. However, to receive data using xingAPI, users log on in an individual Python program. Second, CYBOS plus receives data in a Request/Reply method, and zingAPI receives data through events. It can be thought that these points will show a difference in response time. Response time is important to users who use open APIs. Data were measured from August 5, 2021, to February 3, 2022. For each measurement, 15 repeated measurements were taken to obtain 420 measurements. To increase the accuracy of the study, both APIs were measured alternately under same conditions. A paired t-test was performed to test the hypothesis that the null hypothesis is there was no difference in means. The p-value is 0.2961, we do not reject null hypothesis. Therefore, we can see that there is no significant difference between means. From the boxplot, we can see that the distribution of the response time of eBest is more spread out than that of Cybos, and the position of the center is slightly lower. CYBOS plus has no restrictions on Python programming, but xingAPI has some limits because it indirectly supports Python programming. For example, there is a limit to receiving more than one current price.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.97-104
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• 2021

Securities and investment services had the most data per company on the average, and used the most data. Investors are increasingly demanding to invest through their own analysis methods. Therefore, securities and investment companies provide stock data to investors through open API. The data received using the open API is in text format. Python is effective and convenient for requesting and receiving text data. We investigate there are 22 major securities and investment companies in Korea and only 6 companies. Only Daishin Securities Co. supports Python officially. We compare how to receive stock data through open API using Python, and Python programming features. The open APIs for the study are Daishin Securities Co. and eBest Investment & Securities Co. Comparing the two APIs for receiving the current stock data, we find the main two differences are the login method and the method of sending and receiving data. As for the login method, CYBOS plus has login information, but xingAPI does not have. As for the method of sending and receiving data, Cybos Plus sends and receives data by calling the request method, and the reply method. xingAPI sends and receives data in the form of an event. Therefore, the number of xingAPI codes is more than that of CYBOS plus. And we find that CYBOS plus executes a loop statement by lists and tuple, dictionary, and CYBOS plus supports the basic commands provided by Python.

• 전기전자학회논문지
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• 제8권2호
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• pp.216-223
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• 2004

최근 전자 상거래에서 사용하고 있는 게시판은 고객의 능동적인 참여로 운영되며, 게시물은 고객의 직접적인 의사를 들을 수 있는 인 바운드(Inbound)정보로서 다른 eCRM을 위한 고객 접점 채널 과는 성격이 다른 도구이다. 또한 게시판의 효과적인 운영은 게시판 자체의 신뢰도를 향상 시키고 나아가 전자 상거래 전체의 신뢰도를 높여 줄 수 있는 중요한 eCRM 도구이다. 그러나 현재 대부분의 전자상거래에서 운영하는 게시판은 기 분류된 카테고리를 고객이 직접 수동으로 선정하도록 되어 있고, 이렇게 임의로 분류되는 게시물에 대하여 체계적인 처리 과정 없이 답변이 이루어지기 때문에 답변을 하는데 많은 시간이 소요 되고 있으며, 정확한 답변이 이루어지지 않고 있는 실정이다. 따라서, 본 논문에서는 여러 가지 종류의 게시물에 대하여 나이브 베이지안 분류기를 이용하여 게시판의 기존 문제점의 해결과 효과적인 운영 그리고 게시물의 체계적인 분류 관리를 할 수 있는 게시물 자동 분류기를 설계하고 구현하였다. 아울러 문서 분류 학습 기법 중 대표적인 TFIDF. k-NN, 나이브 베이지안 기법들의 게시물 분류 성능을 측정하여 채택한 나이브 베이지안 분류기의 우수성을 확인 하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제12권1호
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• pp.13-24
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• 2012

This paper applied the Blue Print and FMEA (Failure Mode and Effect Analysis) to education service in order to raise the education service satisfaction. First, the Blue Print is deployed to come up with strategies to overcome the fail possibility point and waiting point. Next, in order to analyze the fail factors and alternative strategies, the Blue Print of education service is applied to FMEA. The results are as follows; first, the ommission from information document by web-mail or e-mail, Second, thing that selected in spite of company uneducated, thing that omitted despite the company is target, and the unsatisfaction of attendee about training contents. Third, the delay of counsel at the telephone reply, erroneous list of course name and attendee at HRD (Human Resource Development), omission of check whether attends or not. Except for unsatisfaction of attendee, all appears at the process that service delivered. And the unsatisfaction of attendee is about education contents. Both is the factor which have influence on the education service quality. The strategies to remove the failure mode are training and manual development on service and work, a thorough management and check of information system like as ERP (Enterprise Resoure Planning), HRD, education institution list DB (Data Base), on-line application system, a development of education program to offer best education that reflect the user needs and continuously changing environment.