Sentiment-Aware Recommendation System for Healthcare using Social Media

TL;DR

This paper introduces a deep learning-based sentiment analysis and suggestion system for healthcare social media content, aiming to provide users with relevant medical information and treatment suggestions efficiently.

Contribution

It presents a novel stacked deep learning model for medical sentiment analysis and a probabilistic approach for recommending treatments based on social media data.

Findings

Effective sentiment classification of medical forum posts

Successful retrieval of similar posts for positive sentiments

Probabilistic treatment suggestions tailored to diseases

Abstract

Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community. The shared content, though informal and unstructured in nature, contains valuable medical and/or health-related information and can be leveraged to produce structured suggestions to the common people. In this paper, at first we propose a stacked deep learning model for sentiment analysis from the medical forum data. The stacked model comprises of Convolutional Neural Network (CNN) followed by a Long Short Term Memory (LSTM) and then by another CNN. For a blog classified with positive sentiment, we retrieve the top-n similar posts. Thereafter, we develop a probabilistic model for suggesting the suitable treatments or procedures for a particular disease or health condition. We believe that integration of medical sentiment and suggestion would be beneficial to the users for finding the relevant contents regarding medications and medical conditions, without having to manually stroll through a large amount of unstructured contents.