Train convolutional network for sentiment analysis. Based on "Convolutional Neural Networks for Sentence Classification" by Yoon Kim, link. Inspired by Denny Britz article "Implementing a CNN for Text Classification in TensorFlow", link.

It turns out that such a small data set as "Movie reviews with one sentence per review" (Pang and Lee, 2005) requires much smaller network than the one introduced in the original article:

• embedding dimension is only 20 (instead of 300; 'CNN-static' still requires ~100 dimensions as it has much fewer trainable weights)
• 2 filter sizes (instead of 3)
• higher dropout probabilities and
• 3 filters per filter size is enough for 'CNN-non-static' (instead of 100)
• embedding initialization does not require prebuilt Google Word2Vec data. Training Word2Vec on the same "Movie reviews" data set is enough to achieve performance reported in the article (81.6%)

Another distinct difference is slidind MaxPooling window of length=2 instead of MaxPooling over whole feature map as in the article

• The Keras Deep Learning library and most recent Theano backend should be installed. You can use pip for that. Not tested with TensorFlow, but should work.