Web Content Mining of Hepatitis-C Community Forums for Identification of Most Frequently Transpiring Lexis

This paper presents the identification of most frequently emerging tokens in the online discussions in community forums on Hepatitis-C, a common disease in many parts of the world. Technically, this has been accomplished by populating the text corpus using the online Web text content. More than 10,000 topics in 2300 posts, collected over a period of seven months, were used for the research purpose. The vector space document model through Bag-of-Words for syntactic parsing and tokenisation or sentence splitting was implemented. The initial number of the lexis was nearly 11,40,000 while removal of noise, stop-words and duplication yielded nearly 15,000 entries. The paper presents the top 100 most frequently transpiring lexis and establishes that the course of treatment, symptoms of the disease and effectiveness of the medicines are key three areas of discussion in the peer online community forum of patients. The results presented here are for Hepatitis-C forum but hold true for other similar diseases as well. To the best of available literature, the paper is first formal attempt to study and analyse the online behaviour of patients.