Topic Modeling of Endocrinology and Metabolism Articles by Iranian Researchers in the Web of Science

Probabilistic topic modeling methods consist of a set of algorithms whose main purpose is to discover the hidden subject structure in a large volume of documents. The purpose of this study is to thematically model the articles of Iranian researchers in the field of endocrinology and metabolism in the citation database of Web of Science.

The present research is of applied type and has been done by text mining and content analysis method. In this study, all required data were retrieved from the Web of Science Citation Database using the keywords registered in the medical subject heading without a time limit until November 6, 2018. Then, using a hidden allocation algorithm, the whole set of documents in MATLAB was analyzed.  

Subject categories were extracted as groups of 20 words in 10 subject categories. Then, by endocrinologists, the subject categories were named based on their relationship to various topics in the field of  endocrinology and metabolism, and each category was assigned a subject title.

The results indicate that the implementation of the latent Dirichlet allocation model has an acceptable performance in presenting the categories of endocrinology and metabolism. The extracted subject categories have good homogeneity and thematic relevance with each  other.