textClassifierHATT.py has the implementation of Hierarchical Attention Networks for Document Classification. Please see the my blog for full detail. Also see Keras Google group discussion

textClassifierConv has implemented Convolutional Neural Networks for Sentence Classification - Yoo Kim. Please see the my blog for full detail.

textClassifierRNN has implemented bidirectional LSTM and one level attentional RNN. Please see the my blog for full detail.

To derive the attention weight which can be useful to identify important words for the classification. Please see my latest update on the post. All you need to do is run a forward pass right before attention layer output. The result is not very promising. I will update the post once I have further result.

This repo is forked from https://github.com/richliao/textClassifier and we find some issue here. So we update the textClassifierHATT with python 2.7 and keras 2.0.8

# clone the repo git clone {repo address} # install Dependent library cd textClassifier pip install -r req.xt # download imdb train from Kaggle in the below link and keep the files in the working directory https://www.kaggle.com/c/word2vec-nlp-tutorial/download/labeledTrainData.tsv # download glove word vector wget http://nlp.stanford.edu/data/glove.6B.zip unzip glove.6B.zip # install nltk 'punkt' using the following code in python interpretor >>>import nltk >>>nltk.download('punkt') # train the model python textClassifierHATT.py # note if in case while installing word2vec, cython error occurs then pip install --upgrade cython 

Enjoy！