Proposed information retrieval systems using LDA topic modeling for answer finding of COVID 19 pandemic: A brief survey of approaches and techniques

Abstract

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA.