본문 바로가기
[학술저널]

  • 학술저널

Minho Kwak(Dankook University) Chelwoo Park(University of Georgia)

DOI : 10.7465/jkdi.2019.30.5.1161

표지

북마크 0

리뷰 0

이용수 0

피인용수 0

초록

The purpose of the study is to demonstrate the prediction quality of logistic regression and artificial neural networks. The main results of the study are the comparisons of the accuracy of both methods. The response variable of the model is a comment assignment by a human rater, and the four predictors are topic proportions estimated from latent Dirichlet allocation. The constructed models for both analyses are mainly concerned with predicting the comment assignment by using the topic proportions as the predictors. The results show that the accuracy of the test data set is generally higher than the accuracy of the cross-validation quality of the logistic regression, and these results are well matched with previous empirical studies. Also, although the use of this accuracy for practical purposes remains still questionable, the results reveal the potential utility the neural network if larger sample size is available in the future.

목차

Abstract
1. Introduction
2. Background on artificial neural network
3. Data analysis
4. Results
5. Conclusion
References

참고문헌(0)

리뷰(0)

도움이 되었어요.0

도움이 안되었어요.0

첫 리뷰를 남겨주세요.
Insert title here