Actionable and Political Text Classification using Word Embeddings and LSTM
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Actionable and Political Text Classification using Word Embeddings and LSTM Adithya Rao, Nemanja Spasojevic Lithium Technologies | Klout
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Transcript of Actionable and Political Text Classification using Word Embeddings and LSTM
Actionable and Political Text Classification using Word
Embeddings and LSTMAdithya Rao, Nemanja Spasojevic
Lithium Technologies | Klout
Main Contributions
● Explore contextual classification problems beyond sentiment:
○ Actionability for customer service
○ Political Leaning on social media
● Deep Learning Models are built with Word Embeddings + LSTM and analyzed
for several languages.
● 85-91 % accuracy for predicting actionability and political leaning.
● Significant improvement over traditional methods.
● Actionability models deployed in production
● Political Leaning model open to download
Online Customer Service
● Sentiment is largely negative in
customer complaints.
● Just knowing sentiment by itself is not
very useful.
● Prioritizing which messages to
respond to can lead to huge cost
savings.
Actionable vs Non-Actionable
Actionable
Non-Actionable
Political Leaning
● Mixed sentiments on various
issues.
● Sentiment towards
candidates are not always
indicative of party lines. eg.
Primaries, #NeverTrump
Political Leaning Examples
Republican
Democrat
Actionability Data● Lithium Response is a
platform for customer
service.
● Labels:
○ If an agent provided a response,
then Actionable.
○ If ignored then Non-Actionable.
● 6 months of data, from Nov
2014 to May 2015
● 12 million training, 3 million
test samples across different
languages
Political Leaning Dataset● Twitter Lists: Use crowdsourced topical
lists to find people with known
Republican or Democrat leaning.
● Sample messages that they posted over a
period of 3 months, between Oct 12th,
2015 to Jan 12th, 2016
● ~330k Training, ~84k Test samples
● List of users available here:
https://github.com/klout/opendata
Deep Network Schematic ● Embedding layer: Maps words to a smaller
n-dimensional vector space.
● LSTM layer: Multiple memory units wirth
gates ~ deep network across timesteps.
● Dropout layer: For regularization
● Fully Connected layer: Learns non-linear
transformations of higher level features.
● Loss Layer: Binary cross-entropy loss
● Learning: Back-propagation of gradients to
train and learn weights.
Language-based Performance
Deep Learning vs Traditional Techniques
LSTM and Embedding Units
Actionability Predictions
Political Leaning Predictions
Political Leaning Predictions (cont.)
Future Work and Improvements
● Analysis of word embeddings, mapping embeddings to word clusters.
● Exploring other architectures with LSTMs and RNNs for training.
● Choosing optimal hyperparameters.
● Time sensitivity of training models for Political Leaning.
Questions?
Thank you!
Contact info:
Adithya Rao [email protected] Spasojevic [email protected]
Github link: https://github.com/klout/opendata
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