  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.
