• boto==2.40.0
• bz2file==0.98
• gensim==0.12.4
• numpy==1.11.0
• protobuf==3.0.0b2
• requests==2.10.0
• scipy==0.17.1
• six==1.10.0
• smart-open==1.3.3
• tensorflow==0.8.0

WITHOUT pre-trained w2v

• Train

   cd cnn
   nohup python cnn_train.py > out.txt &

• Test

  • Modify cnn/cnn_test.py

     savepath = 'model_path/model-xxxx'

  • Run test script

     cd cnn
     python cnn_test.py

WITH pre-trained w2v

• Download and extract the compressed file to have the pre-trained w2v bin file

  • download:link1

• Modify w2v_cnn/cnn_train.py

   model_path = 'path_to_w2v_bin/word2vec_twitter_model.bin'

• Train

   cd w2v_cnn
   nohup python cnn_train.py > out.txt &

• Test

  • Modify w2v_cnn/cnn_test.py

     savepath = 'model_path/model-xxxx'

  • Run test script

     cd w2v_cnn
     python cnn_test.py

Dev (semeval16_T4A_devtest_npo)

• number of data: 1588

Tst (semeval16_T4A_test_npo)

• number of data: 20632

Trn (semeval13_T2B_16T4A_train_dev_npo)

• number of data: 15385

Data files

• semeval13_T2B_16T4A_train_dev_devtest_npo - 1588+15385 = 16973
• semeval16_T4A_devtest_npo = 1588
• semeval13_T2B_16T4A_train_dev_npo = 15385
• semeval16_T4A_dev_npo = 1595
• semeval16_T4A_test_npo = 20632
• semeval16_T4A_train_npo = 4796

Format of data (TAB separated)

• 'objective': [0, 1, 0], 1
• 'positive': [0, 0, 1], 2
• 'negative': [1, 0, 0], 0

• download:link
• reference1:github
• reference2:intro