Comparison of Topic Modeling Algorithms
Publication Date : 27/11/2019
We present a topic identification system for news, which is based upon an evaluation of similarity between the topics and a large amount of documents in the news database. Our system is able to provide the topics for every news samples. The system implements and compares the two Topic Models, Latent Dirichlet Allocation (LDA) and Latent Semantic Allocation (LSA), on a news database containing eleven thousand documents. The topic models behaviour has been examined on the basis of standard metrics, accuracy and the implementation speed of the algorithms.
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