  In this work, we apply word embeddings and neural networks with Long
Short-Term Memory (LSTM) to text classification problems, where the
classification criteria are decided by the context of the application. We
examine two applications in particular. The first is that of Actionability,
where we build models to classify social media messages from customers of
service providers as Actionable or Non-Actionable. We build models for over 30
different languages for actionability, and most of the models achieve accuracy
around 85%, with some reaching over 90% accuracy. We also show that using LSTM
neural networks with word embeddings vastly outperform traditional techniques.
Second, we explore classification of messages with respect to political
leaning, where social media messages are classified as Democratic or
Republican. The model is able to classify messages with a high accuracy of
87.57%. As part of our experiments, we vary different hyperparameters of the
neural networks, and report the effect of such variation on the accuracy. These
actionability models have been deployed to production and help company agents
provide customer support by prioritizing which messages to respond to. The
model for political leaning has been opened and made available for wider use.
