• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.491-497
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• 2009

This paper proposes a new feature weighting method for document sentiment classification. The proposed method considers the difference of sentiment intensities among sentences in a document. Sentiment features consist of sentiment vocabulary words and the sentiment intensity scores of them are estimated by the chi-square statistics. Sentiment intensity of each sentence can be measured by using the obtained chi-square statistics value of each sentiment feature. The calculated intensity values of each sentence are finally applied to the TF-IDF weighting method for whole features in the document. In this paper, we evaluate the proposed method using support vector machine. Our experimental results show that the proposed method performs about 2.0% better than the baseline which doesn't consider the sentiment intensity of a sentence.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1525-1536
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• 2016

ROTC is the system that selects the best students in university and implements the military training of two years and uses them as beginning commander of the army by commissioning as officers. ROTC makes up a very large portion of the army and society in Korea in terms of the size and role. Therefore, it is very important to enhance intangible combat power of the Korean army by improving leadership of primary grade officer from ROTC. The purpose of this paper is to investigate whether or not ROTC candidate training program is providing the right training, so as to improve ROTC's leadership and present improvement plan. First, we divide ROTC candidate training program into three areas and take the 1st and 2nd survey. Then we compare and analyze the actual condition and recognition of investigation using statistical analysis methods such as chi-square test, ANOVA and Duncan's multiple range test. Also, we analyze the element affecting the army leader's three qualities formation which are basic elements of ROTC cadet's leadership using multiple regression analysis.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.407-415
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• 2015

$Na{\ddot{i}}ve$ Bayes Classification is based on input variables that are a conditionally independent given output variable. The $Na{\ddot{i}}ve$ Bayes assumption is unrealistic but simplifies the problem of high dimensional joint probability estimation into a series of univariate probability estimations. Thus $Na{\ddot{i}}ve$ Bayes classier is often adopted in the analysis of massive data sets such as in spam e-mail filtering and recommendation systems. In this paper, we propose a variable selection method based on ${\chi}^2$ statistic on input and output variables. The proposed method retains the simplicity of $Na{\ddot{i}}ve$ Bayes classier in terms of data processing and computation; however, it can select relevant variables. It is expected that our method can be useful in classification problems for ultra-high dimensional or big data such as the classification of diseases based on single nucleotide polymorphisms(SNPs).

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• Industry Promotion Research
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• v.5 no.1
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• pp.13-20
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• 2020

This study is about consumer preference of plastic toilets that can provide automatic supply of recycled water. First, the preference for plastic toilet seat design by gender and age group was preferred for gender type C and G for the sex. As the result of the chi-square test, the significance probability is .044 and the significance is P <.0 5. I could confirm that. Age, teens, 40 s, and 50 s or older prefer type C, 20 s and 30 s, but B type is not statistically significant. Second, the differences among the groups of preference for appearance design criteria according to general characteristics were all stable (stable appearance) in gender, age, region, education, and salary, but the chi-square test showed that they were not statistically significant. There was no difference between them. This study has implications for improving competitiveness and productivity by reducing the main production cost by commercializing toilets made of plastic materials.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.437-444
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• 2013

The object of study is to verify the distributions of response packets of 3 portal sites such as Naver, Daum, Nate. The period of experiments is from May 19th 2010 to November 7th 2012 and the number of experiments is 4,642. The distributions of Naver, Nate are biomodals. The distribution of Daum has long right tails. 3 distributions are different under 1% significance level using chi-square test and two sample Kolmogorov-Smirnov test. From proportions and percentiles, Naver has a distribution with the largest values. Nate is the second place, and Daum has a distribution with the smallest values. We must make portal pages light to increase response speed including other technologies. We expect our results to activate competition among portal sites.

• Journal of Korean Society of Disaster and Security
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• v.10 no.1
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• pp.11-17
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• 2017

The case study on ground subsidence was conducted and the cause of ground subsidence was evaluated, main cause were insufficient site exploration, inaccurate strength parameters, defective temporary wall, insufficient reaction for boiling and heaving, excessive excavation and so on. Risk factors during excavation were identified from the cause of ground subsidence and risk factors were site exploration, selecting excavation method, structure analysis, measurement plan, excavation method construction, underground water level change, natural disaster and construction management. The survey of the experts on risk factors identified was conducted to evaluate the importance of risk factors, and confidence analysis was performed to evaluate the significance level between survey result and survey respondent using Chi-square Test.

• Asia Marketing Journal
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• v.9 no.2
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• pp.23-47
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• 2007

A new chi-square test is proposed to assess significance of attributes for nonparametric conjoint models. The key idea is to form subsets of rankings and test the dependence between the attribute levels and the sets of rankings. The null hypothesis states that the rankings for profiles with the focal attribute are distributed randomly among the sets of rankings. The approach is simple, easy to use, and can be applied at the individual level as well as at the aggregate level. It can be used for the trade-off approach as well as for the full profile approach.

• The Science & Technology
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• v.18 no.6 s.193
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• pp.51-54
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• 1985

피어슨이 개발한 '카이제곱검정'은 그 자체로 본다면 하나의 사소한 사건이었으나 우리의 숫자세계를 해석하는 방법에서 하나의 전환을 구획하는 신호가 되었다. 오늘날 아이디어를 정책수립가들과 일반에게 제시하는 하나의 표준방법이 될 수 있을 것이다.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.179-188
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• 1996

복합조사표본추출법(complex survey sampling)에 근거한 분할표분석에 카이제곱검정법을 사용할 때의 문제점들과 해결방법들을 살펴보았다. 나아가, 군집표본추출의 경우에 붓스트랩방법의 타당성을 보였으며, 실제자료분석을 통하여 실제 활용가능성과 잇점을 제시하였다.