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Perceptron Sentiment Classification of Text

1. Single Perceptron Sentiment Classification

  • Pre-processing: lowercasing, frequency of word count to limit vocabulary, NLTK Snowball stemming
  • Unigram Feature
  • Unigram and Bigram Features

2. Multilayer Perceptron Sentiment Classification

  • two layer with tanh activation layer, with sigmoid activation for final output (used BCE loss)

3. Recurrent Neural Networks

  • Bi-directional LSTM with "max pooling" activation to a linear fully connected layer. Then, this is fed to final Sigmoid activation.

4. RCNN

  • Bi-directional LSTM with convolution and max pooling.