Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks
This work addresses computational efficiency in sentiment analysis for social media data, but it is incremental as it builds on existing LSTM methods with minor modifications.
The authors tackled sentiment analysis on a GOP Debate Twitter dataset by proposing six parameter-reduced slim LSTM models to speed up training and reduce computational costs, evaluating two of them and comparing performance with standard LSTM, including studying bidirectional layers and hyperparameter tuning.
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.