Lab41's foray into Sentiment Analysis with Deep Learning. In addition to checking out the source code, visit the Wiki for Learning Resources and possible Conferences to attend.
Try them, try them, and you may! Try them and you may, I say.
- Blog Overviews
- Docker Environments
- Binary Classification with Word Vectors
- Word Vector Models
- Training and Testing Data
- Binary Classification via Deep Learning
- CNN
- LSTM
lab41/itorch-[cpu|cuda]: iTorch IPython kernel for Torch scientific computing GPU frameworklab41/keras-[cpu|cuda|cuda-jupyter]: Keras neural network library (CPU or GPU backend from command line or within Jupyter notebook)lab41/neon-[cuda|cuda7.5]: neon Deep Learning framework (with CUDA backend) by Nervanalab41/pylearn2: pylearn2 machine learning research librarylab41/sentiment-ml: build word vectors (Word2Vec from gensim; GloVe from glove-python), tokenize Chinese text (jieba and pypinyin), and tokenize Arabic text (NLTK and Stanford Parser)lab41/mechanical-turk: convert CSV of Arabic tweets to individual PNG images for each Tweet (to avoid machine-translation of text) and auto-submit/score Arabic sentiment survey via AWS Mechanical Turk
python -m benchmarks/baseline_classifiers
| model | filename | filesize | vocabulary | details |
|---|---|---|---|---|
| Sentiment140 | sentiment140_800000.bin | 153M | 83,586 | gensim Word2Vec(size=200, window=5, min_count=10) |
| Open Weiboscope | openweibo_fullset_hanzi_CLEAN_vocab31357747.bin | 56G | 31,357,746 | jieba-tokenized Hanzi Word2Vec(size=200, window=5, min_count=1) |
| Open Weiboscope | openweibo_fullset_min10_hanzi_vocab2548911.bin | 4.6G | 2,548,911 | jieba-tokenized Hanzi Word2Vec(size=200, window=5, min_count=10) |
| Arabic Tweets | arabic_tweets_min10vocab_vocab1520226.bin | 1.2G | 1,520,226 | Stanford Parser-tokenized Word2Vec(size=200, window=5, min_count=10) |
| Arabic Tweets | arabic_tweets_NLTK_min10vocab_vocab981429.bin | 759M | 981,429 | NLTK-tokenized Word2Vec(size=200, window=5, min_count=10) |
| train/test set | filename | filesize | details |
|---|---|---|---|
| Sentiment140 | sentiment140_800000_samples_[test/train].bin | 183M | 80/20 split of 1.6M emoticon-labeled Tweets |
| Open Weiboscope | openweibo_hanzi_censored_27622_samples_[test/train].bin | 25M | 80/20 split of 55,244 censored posts |
| Open Weiboscope | openweibo_800000_min1vocab_samples_[test/train].bin | 564M | 80/20 split of 1.6M deleted posts |
| Arabic Twitter | arabic_twitter_1067972_samples_[test/train].bin | 912M | 80/20 split of 2,135,944 emoticon-and-emoji labeled Tweets |
Character-by-character processing From Zhang and LeCun's Text Understanding From Scratch:
#Set Parameters for final fully connected layers
fully_connected = [1024,1024,1]
model = Sequential()
#Input = #alphabet x 1014
model.add(Convolution2D(256,67,7,input_shape=(1,67,1014)))
model.add(MaxPooling2D(pool_size=(1,3)))
#Input = 336 x 256
model.add(Convolution2D(256,1,7))
model.add(MaxPooling2D(pool_size=(1,3)))
#Input = 110 x 256
model.add(Convolution2D(256,1,3))
#Input = 108 x 256
model.add(Convolution2D(256,1,3))
#Input = 106 x 256
model.add(Convolution2D(256,1,3))
#Input = 104 X 256
model.add(Convolution2D(256,1,3))
model.add(MaxPooling2D(pool_size=(1,3)))
model.add(Flatten())
#Fully Connected Layers
#Input is 8704 Output is 1024
model.add(Dense(fully_connected[0]))
model.add(Dropout(0.5))
model.add(Activation('relu'))
#Input is 1024 Output is 1024
model.add(Dense(fully_connected[1]))
model.add(Dropout(0.5))
model.add(Activation('relu'))
#Input is 1024 Output is 1
model.add(Dense(fully_connected[2]))
model.add(Activation('sigmoid'))
#Stochastic gradient parameters as set by paper
sgd = SGD(lr=0.01, decay=1e-5, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, class_mode="binary")# initialize the neural net and reshape the data
model = Sequential()
model.add(Embedding(max_features, embedding_size)) # embed into dense 3D float tensor (samples, maxlen, embedding_size)
model.add(Reshape(1, maxlen, embedding_size)) # reshape into 4D tensor (samples, 1, maxlen, embedding_size)
# convolution stack
model.add(Convolution2D(nb_feature_maps, nb_classes, filter_size_row, filter_size_col, border_mode='full')) # reshaped to 32 x maxlen x 256 (32 x 100 x 256)
model.add(Activation('relu'))
# convolution stack with regularization
model.add(Convolution2D(nb_feature_maps, nb_feature_maps, filter_size_row, filter_size_col, border_mode='full')) # reshaped to 32 x maxlen x 256 (32 x 100 x 256)
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2))) # reshaped to 32 x maxlen/2 x 256/2 (32 x 50 x 128)
model.add(Dropout(0.25))
# convolution stack with regularization
model.add(Convolution2D(nb_feature_maps, nb_feature_maps, filter_size_row, filter_size_col)) # reshaped to 32 x 50 x 128
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2))) # reshaped to 32 x maxlen/2/2 x 256/2/2 (32 x 25 x 64)
model.add(Dropout(0.25))
# fully-connected layer
model.add(Flatten())
model.add(Dense(nb_feature_maps * (maxlen/2/2) * (embedding_size/2/2), fully_connected_size))
model.add(Activation("relu"))
model.add(Dropout(0.50))
# output classifier
model.add(Dense(fully_connected_size, 1))
model.add(Activation("sigmoid"))
# try using different optimizers and different optimizer configs
model.compile(loss='binary_crossentropy', optimizer='adam', class_mode="binary")