/Sentiment-Analysis

Primary LanguagePython

Introduction

  • A classification of Sentiment Analysis which is implemented by pytorch.
  • There are many data in data, *.txt of that are came from sent-conv-torch, *.conll.out of that are generated from our lab's parser.
    • CR
    • MPQA
    • TREC
    • MR
    • SUBJ
  • My processed data by preprocessed.sh will be saved in processed_data.
  • Trere are many models in models.
    • Pooling
    • CNN
    • Multi_Channel_CNN
    • Multi_Layer_CNN
    • CharCNN
    • GRU
    • LSTM
    • LSTM_CNN
    • TreeLSTM
    • CNN_TreeLSTM
    • LSTM_TreeLSTM
    • Transformer(TODO)
  • My log will be saved in log.
  • Trere are many scripts in utils.
    • Alphabet.py which is used to build dictionary.
    • Common.py which is saved unk-key and pad-key.
    • Embedding.py which is used to load pre_train embedding by Yang Song.
    • Evaluate.py which is used to calculate the F1.
    • Feature.py which is implemented a sentence's features, including word, word_id, label, root and so on.
    • build-batch.py which is used to build the data's mini batch.
    • log.py which is used to save the log.
    • opts.py
    • which is implemented the argparses.
    • trainer.py which is used to train the data.
    • tree.py which is implemented the tree's methods.

Requirement

    python : 3.5+
    pytorch : 0.4.0
    cuda : 8.0 (support GPU, you can choose)

Usage

  • first step

      sh preprocess.sh

  • second step

      sh run.sh

Result

Data/Model TREC SUBJ MR CR MPQA
Pooling 76.12 89.58 74.51 80 86.43
CNN 90.4 91.98 77.73 84.38 88.96
Char_CNN 91.52 93.33 78.91 84.38 86.33
Multi_Channel_CNN 89.06 94.06 79.59 83.12 88.48
Multi_Layer_CNN 91.74 93.65 79 84.06 89.36
LSTM 89.73 92.5 80.57 83.75 89.65
LSTM_CNN 92.63 92.19 81.05 83.12 89.16
GRU 89.06 92.6 79.1 83.44 89.75
TreeLSTM 89.18 91.09 78.54 81.4 89.28
CNN_TreeLSTM - - 79.11 - -

Question

If you have any questions about this code, please report to me immediately by zenrran@qq.com!