Tensorflow implementation of RNN(Recurrent Neural Network) for sentiment analysis, one of the text classification problems. There are three types of RNN models, 1) Vanilla RNN, 2) Long Short-Term Memory RNN and 3) Gated Recurrent Unit RNN.

• Movie reviews with one sentence per review. Classification involves detecting positive/negative reviews (Pang and Lee, 2005)
• Download "sentence polarity dataset v1.0" at the Official Download Page
• Located in "data/rt-polaritydata/" in my repository
• rt-polarity.pos contains 5331 positive snippets
• rt-polarity.neg contains 5331 negative snippets

• positive data is located in "data/rt-polaritydata/rt-polarity.pos"

• negative data is located in "data/rt-polaritydata/rt-polarity.neg"

• "GoogleNews-vectors-negative300" is used as pre-trained word2vec model

• Display help message:

   $ python train.py --help

• Train Example:

  1. Vanilla RNN

  

   $ python train.py --cell_type "vanilla" \
   --pos_dir "data/rt-polaritydata/rt-polarity.pos" \
   --neg_dir "data/rt-polaritydata/rt-polarity.neg"\
   --word2vec "GoogleNews-vectors-negative300.bin"

  2. Long Short-Term Memory (LSTM) RNN

  

   $ python train.py --cell_type "lstm" \
   --pos_dir "data/rt-polaritydata/rt-polarity.pos" \
   --neg_dir "data/rt-polaritydata/rt-polarity.neg"\
   --word2vec "GoogleNews-vectors-negative300.bin"

  3. Gated Reccurrent Unit (GRU) RNN

  

   $ python train.py --cell_type "gru" \
   --pos_dir "data/rt-polaritydata/rt-polarity.pos" \
   --neg_dir "data/rt-polaritydata/rt-polarity.neg"\
   --word2vec "GoogleNews-vectors-negative300.bin"

• Movie Review dataset has no test data.

• If you want to evaluate, you should make test dataset from train data or do cross validation. However, cross validation is not implemented in my project.

• The bellow example just use full rt-polarity dataset same the train dataset

• Evaluation Example:

   $ python eval.py \
   --pos_dir "data/rt-polaritydata/rt-polarity.pos" \
   --neg_dir "data/rt-polaritydata/rt-polarity.neg" \
   --checkpoint_dir "runs/1523902663/checkpoints"

• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales (ACL 2005), B Pong et al. [paper]
• Long short-term memory (Neural Computation 1997), J Schmidhuber et al. [paper]
• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (EMNLP 2014), K Cho et al. [paper]
• Understanding LSTM Networks [blog]
• RECURRENT NEURAL NETWORKS (RNN) – PART 2: TEXT CLASSIFICATION [blog]