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. For "CNN-rand" and "CNN-non-static" gets to 88-90%, and "CNN-static" - 85%

• larger IMDB corpus, longer sentences; sentence length is very important, just like data size
• smaller embedding dimension, 20 instead of 300
• 2 filter sizes instead of original 3
• much fewer filters; experiments show that 3-10 is enough; original work uses 100
• random initialization is no worse than word2vec init on IMDB corpus
• sliding Max Pooling instead of original Global Pooling

• 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.