Leveraging Medical Sentiment to Understand Patients Health on Social Media

TL;DR

This paper analyzes social media posts on health forums to understand patient sentiments about medical conditions, proposing a CNN-SVM model with medical sentiment features that outperforms existing methods.

Contribution

It introduces a benchmark dataset for medical sentiment analysis and a novel CNN-SVM architecture incorporating medical sentiment features.

Findings

Medical sentiment features improve classification accuracy.

Model outperforms state-of-the-art on benchmark datasets.

Effective for fine-grained medical condition detection.

Abstract

The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text. A large percentage of this population is actively engaged in health social networks to share health-related information. In this paper, we address an important and timely topic by analyzing the users' sentiments and emotions w.r.t their medical conditions. Towards this, we examine users on popular medical forums (Patient.info,dailystrength.org), where they post on important topics such as asthma, allergy, depression, and anxiety. First, we provide a benchmark setup for the task by crawling the data, and further define the sentiment specific fine-grained medical conditions (Recovered, Exist, Deteriorate, and Other). We propose an effective architecture that uses a Convolutional Neural Network (CNN) as a data-driven feature extractor and a Support Vector Machine (SVM) as a classifier. We further develop a sentiment feature which is sensitive to the medical context. Here, we show that the use of medical sentiment feature along with extracted features from CNN improves the model performance. In addition to our dataset, we also evaluate our approach on the benchmark "CLEF eHealth 2014" corpora and show that our model outperforms the state-of-the-art techniques.