Automated System for Opinion Detection of Breathing Problem Discussions in Medical Forum Using Deep Neural Network (2023)

Abstract: 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%.

## introduction opinionated feedback @xcite specifically in medical interactions has the remarkable capability to affect public sentiments towards better healthcare. people use medical forums or medical blogs to easily access health-related information and to get mental support from people in similar situations. it also fascinates the practitioners, medical experts, and medical representatives for their better personal, societal, and business enhancements @xcite . the original poster (op) posts with confusion, stress, or anxiety; then, the sentiment changes by the replies of the respondent posters (rp) and ultimately stops. the sentiment expressed has two major predictors: time and author and different aspects of them (van @xcite . these long illustrated online honest confessions with detailed briefing are better than in-person conversations @xcite and create computational health mines @xcite . this facilitates the intervention of the mining experts @xcite , who can help in greatly reducing healthcare costs, by more than $300 per year @xcite . the government initiatives are also evident regarding the sharing of information among patients @xcite , opinion against vaccination @xcite , adhering to agency-level complaints @xcite etc. close to 47% of the online users @xcite looking for mainly three types of support from these online health communities (ohc). these are support by information, support by emotion, and support by companionship @xcite . but, quality assessments to improve the quality of life (qol) of these supports, is difficult due to the contributions from diversified knowledge retainers @xcite . also, facts and experience can play a pivotal role in such cases @xcite . these posts not only help in interacting with the most influencing participants known as opinion leaders @xcite , but also allow to form of small groups with the same opinion, called echo chambers @xcite . the basic category of sentiments, experience, facts, and opinions associated with this unstructured and noisy information differs by the use of functional keywords @xcite and behavioral patterns @xcite . this requires intelligent questionnaires @xcite and careful intervention of medical lexicons @xcite , to alleviate the time and processing complexity. instead of having potential scopes in applica-tions like consumer satisfaction, relation extraction, change of role, regular and comparative opinions detection, and opinion polarization, there exist multiple challenges in maintaining and controlling the posts, engaging more authors than visitors, delay in reply, ability to inherit the conceptions and postcompletion feedback @xcite @xcite . we have closely observed the structure of the forum and found that under the section "condition and medicine categories", there are a total of 30 categories. we have chosen the category "chest and lungs". there are a total of 11,754 members and 64,125 posts in 15 groups (as of 07.11.2023). each of these groups is associated with some discussions, distributed over multiple pages. each discussion is associated with a discussion title, original post, and replies, and by default sorted by oldest. the original post mostly bears negative sentiments like depression, fear, anxiety, sorrow, helplessness, frustration, etc. an op expects a most-voted comment to be of a person who has recovered from a similar situation in a guided way. thus, a positive comment must be associated with self-experience and suggestions from an rp. but, op may ignore a comment, sharing mere sympathy or a comment from a person yet to recover. thus, for each post statement (p) by the original poster (op), satisfying the minimum number of replies, we have to automatically fetch the maximum voted comment (q) and determine the opinion expressed in q as positive, negative, or neutral based on information support, self-experience, and emotional support. we have contributed to this paper in the following ways: 1) filter-based automatic scrapping of posts to consider posts with maximum votes only. 2) annotating based on three key components, selfexperience, suggestion, and disease recovery. 3) stacked bilstm model for opinion classification. ## related work the landscape of opinions associated with health (physical or mental), medicine (good or bad), treatment (working or not), and medication (effective or not) encourages verse range of initiatives. ## conclusion using this automated system we can successfully scrape, and determine the sentiment of the maximum voted reply against a discussion with satisfactory accuracy in minimum system requirefigure 6: training accuracy vs training loss ments. since a reply post can be made either directly against the original post or reply to another respondent, it will help both the op and others (visitors and members) to automatically track the most agreed-upon comment and its opinion.