Topic Modeling and its Application in Research: A Review of Specialized Literature

Topic modeling is one of the text mining techniques that allows you to discover unknown topics in a collection of documents, interpret documents based on these topics, and use these interpretations to organize, summarize, and search for texts automatically. Familiarity with the concept and technique of topic modeling, and its application in discovering topics and organizing information is one of the main goals of this research.

The present study is a review-analytical type in which, while introducing topic modeling, it has categorized and reviewed the applications of this technique based on its performance and provided a sample of research that has used this technique.

Topic modeling algorithms is used not only in addition to the three main objectives of discovering hidden topics, interpreting documents based on topics, and finally organizing and classifying texts, but also is used in discovering hidden topics and relationships in the fields of science, information retrieval, categorizing documents based on topics, discovering outstanding patterns and emerging events, clustering the concepts of scientific fields, analyzing the course of conceptual evolution during historical periods, determining the hierarchical relationships of concepts. A specific scientific field or field and vocabulary enrichment.

Topic modeling based on machine learning and artificial intelligence knowledge has been proposed as one of the new approaches to organizing information resources and serious studies are being conducted in this field. Therefore, by using topic modeling algorithms in order to automate the extraction of the subject and discover the hidden issues in the source, it is possible to strengthen and update the new systems of organizing information resources.