Actionable and Political Text Classification Using Word Embeddings and LSTM
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Description
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.
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