期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2020
卷号：11
期号：6
页码：943-947
DOI：10.21817/indjcse/2020/v11i6/201106239
出版社：Engg Journals Publications
摘要：Large collections of documents are readily available online and widely accessed by diverse communities. Topic models can extract surprisingly interpretable and useful structure without any explicit “understanding” of the language by computer. The objective of this work to implement the leading machine learning algorithms , to get the optimal model through the one of the leading metric Matthew Correlation Coefficient. This work shows that the accuracies of the NaiveBayesMultinomialText classifier produces 64.59% level of accuracy, IBK classifier is 99.65% level of accuracy,AdaBoostM1classifier is 99.36% and ZeroR classifier is 64.60% and DecisionStump classifier is 72.22%. The DecisionStump algorithm, Instance based classifier and AdaBoost Classifiers are correlated positively, but this proposed system recommends that AdaBoost Classifier and IBK classifers are strongly correlated with this model.
关键词：Topic Modeling;Correlation;Binary Classification;and Multi Classification
