Topic modeling with latent Dirichlet allocation for cancer disease posts

Abstract

Purpose: The aim of this paper is to reveal the main topics discussed by examining reddit user comments about cancer disease. Theory and Methods: After the preproccesing, user comments are divided into topics with the help of the latent dirichlet allocation method. Results: The proposed approach using LDA has created consistent and semantically meaningful topics and clusters from user shares. The obtained topics can not only help people to interpret the texts in a large sharing collection in a way that can be interpreted by human beings but can also help patients and doctors discover new content that may be neglected. Conclusion: The results obtained with the LDA algorithm consist of the diagnosis of cancer disease, treatment process, moral-motivation during the disease period, chemotherapy period and medical support.