Estimating Number of Topics in Topic Modeling on Persian Research Articles

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Journal Paper

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Journal: Iranian Journal of Information Processing & Management Year:2023 | Volume:38 | Issue:4 Page(s): 1345-1368

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Title

Estimating Number of Topics in Topic Modeling on Persian Research Articles

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Keywords

Renormalization Theory

Rényi Entropy

Grid Search

Latent Dirichlet Allocation

Abstract

This article presents a method to find the number of topics in Persian research articles, which is actually one of the main challenges in topic modeling. It is the process of automatically recognizing topics in a text with the aim of discovering hidden patterns. This study has estimated the number of topics for Persian research articles using two approaches. The first is based on the greedy search and later uses Renormalization Theory, which is a mathematical formalism to construct a procedure for changing the scale of the system so that the behavior of the system preserves. Also, the execution time of both algorithms on Persian academic articles has been compared with each other. The findings indicate that the renormalization approach predicts the number of topics in Persian research articles with the lower time complexity in comparison to the greedy based approach. The approach based on Renormalization has high efficiency for estimating the number of topics in Persian academic articles.

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