  Health related social media mining is a valuable apparatus for the early
recognition of the diverse antagonistic medicinal conditions. Mostly, the
existing methods are based on machine learning with knowledge-based learning.
This working note presents the Recurrent neural network (RNN) and Long
short-term memory (LSTM) based embedding for automatic health text
classification in the social media mining. For each task, two systems are built
and that classify the tweet at the tweet level. RNN and LSTM are used for
extracting features and non-linear activation function at the last layer
facilitates to distinguish the tweets of different categories. The experiments
are conducted on 2nd Social Media Mining for Health Applications Shared Task at
AMIA 2017. The experiment results are considerable; however the proposed method
is appropriate for the health text classification. This is primarily due to the
reason that, it doesn't rely on any feature engineering mechanisms.
