Classification of New Titles by Two Stage Latent Dirichlet Allocation - Guven, Zekeriya Anil; Diri, Banu; Cakaloglu, Tolgahan (IEEE, 2018)
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.
Keywords: Topic Modelling; Latent Dirichlet Allocation; Natural Language Processing; New Analysis; Machine Learning; Computer Science; Engineering