Chest X-ray radiology majorly focuses on diseases like consolidation, pneumothorax, pleural effusion, lung collapse, etc., causing breathing and circulation problems. A tendency to share such problems in the forums for an answer without revealing personal demographics is also very common. However, we have observed more visitors than authors, which leads to a very poor average reply per discussion (3 to 12 only), and also many left with no or late replies in the forums. To alleviate the process, and ease of acquiring the best replies from multiple discussions, we propose a supervised learning framework by automatic scrapping and annotation of breathing problem-related group discussions from the patient.info 1 forum and determine the associated sentiment of the most voted respondent post using Bi-LSTM. We assume the most voted reply is the most factual and experienced. We mainly scrapped and determined the sentiment of bronchiectasis, asthma, pneumonia, and respiratory diseaserelated posts. After filtering and augmentation, a total of 1,748 posts were used for training our Stacked Bi-LSTM model and achieved an overall accuracy of 90%.