Gated Recurrent Units for Airline Sentiment Analysis of Twitter Data
We explore the use of a bi directional gated recurrent unit (GRU) network for sentiment analysis of Twitter data directed at U.S airlines. The performance of the GRU is then compared with a convolutional neural network (CNN) and also the comparatively simpler vanilla single layer network. We wish to not only identify which model has the highest test accuracy, but also its precision, recall rates as well as its robustness to unbalanced classes in the training set. We also discuss the common practice of re weighting data for unbalanced classes and its effect on a model's prediction from a Bayesian viewpoint. Indeed, as it turns out, there is a balance to be found between using local information, that is information contained in each tweet and global information, the information present in the entire training set or test set when making predictions. We also implement a Long Short Term Memory (LSTM) network for text generation of some positive or negative airline tweets and the relatively coherent text demonstrate the power of these networks.