  LSTM or Long Short Term Memory Networks is a specific type of Recurrent
Neural Network (RNN) that is very effective in dealing with long sequence data
and learning long term dependencies. In this work, we perform sentiment
analysis on a GOP Debate Twitter dataset. To speed up training and reduce the
computational cost and time, six different parameter reduced slim versions of
the LSTM model (slim LSTM) are proposed. We evaluate two of these models on the
dataset. The performance of these two LSTM models along with the standard LSTM
model is compared. The effect of Bidirectional LSTM Layers is also studied. The
work also consists of a study to choose the best architecture, apart from
establishing the best set of hyper parameters for different LSTM Models.
