Intelligently extracting knowledge from social media has newly attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare result and moderate costs using consumer-generated viewpoint. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users’ forum posts, and identifies user communities and influential users for the determination of ascertaining user opinion of cancer treatment. We used a Self Organizing Map to analyze word frequency data derived from users’ forum posts. We then introduced a novel network-based approach for modeling users’ forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and analyses influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide speedy, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.