Classification of New Titles by Two Stage Latent Dirichlet Allocation

被引：0

作者：

Guven, Zekeriya Anil ^{[1
]}

Diri, Banu ^{[2
]}

Cakaloglu, Tolgahan ^{[3
]}

机构：

[1] Recep Tayyip Erdogan Univ, Bilisim Sistemleri Muhendisligi, Rize, Turkey

[2] Yildiz Tekn Univ, Bilgisayar Muhendisligi, Istanbul, Turkey

[3] Univ Arkansas, Dept Comp Sci, Fayetteville, AR 72701 USA

来源：

2018 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS CONFERENCE (ASYU) | 2018年

关键词：

Topic Modelling; Latent Dirichlet Allocation; Natural Language Processing; New Analysis; Machine Learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the rapid development of the Internet, thousands of different news reports from different channels are presented to us. So much news, particularly in the media sector, is an important question to be categorized and archived without human effort. In this study, it is aimed to be able to determine which news item belongs to large news headlines collected from news sites. For this, a two stage method is proposed, which is based on the classical Latent Dirichlet Allocation (LDA) algorithm used in the model. With the developed two stage LDA method, comparison of the conventional LDA was made. Then, by creating a file with an arff extension from the word weights of the topics, the success of the machine learning methods in Weka was measured.

引用

页码：99 / 103

页数：5

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