I'm working on a sentiment analysis project in python with keras using CNN and word2vec as an embedding method I want to detect positive, negative and neutral tweets(in my corpus I considered every negative tweets with the 0 label, positive = 1 and neutral = 2). Since I'm new in this field I have some questions, here is a part of my code: ***Assuming that X-train and X-test contain tweets and Y-train and Y-test contain tweet's labels.
if i < train_size:
if labels[index] == 0 :
Y_train[i, :] = [1.0, 0.0]
elif labels[index] == 1 :
Y_train[i, :] = [0.0, 1.0]
else:
Y_train[i, :] = [1.0, 1.0]
else:
if labels[index] == 0 :
Y_test[i - train_size, :] = [1.0, 0.0]
elif labels[index] == 1 :
Y_test[i - train_size, :] = [0.0, 1.0]
else:
Y_test[i - train_size, :] = [1.0, 1.0]
in the code above you see that I considered if a related label was 0(if labels[index] == 0 :) as negative I put [1.0, 0.0] in some specific list and if the label was 1(if labels[index] == 1 :) I put [0.0, 1.0] as positive tweets and else (if labels[index] == 2 :) as neutral i put [1.0, 1.0] so just consider that the logical part af my code that i mentioned is ok.
here is my keras model:
model = Sequential()
model.add(Conv1D(32, kernel_size=3, activation='elu',
padding='same', input_shape=
(max_tweet_length,vector_size)))
model.add(Conv1D(32, kernel_size=3, activation='elu',
padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='elu',
padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='elu',
padding='same'))
model.add(Dropout(0.25))
model.add(Conv1D(32, kernel_size=2, activation='elu',
padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu',
padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu',
padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu',
padding='same'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='tanh'))
model.add(Dense(256, activation='tanh'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))
So in order to predict a new input, I have this code:
sentiment = model.predict(np.array(a),batch_size=1,verbose = 2)[0]
if(np.argmax(sentiment) == 0):
print("negative")
print('the label is')
print(np.argmax(sentiment))
elif (np.argmax(sentiment) == 1):
print("positive")
print('the label is')
print(np.argmax(sentiment))
elif (np.argmax(sentiment) ==2):
print("neutral")
print('the label is')
print(np.argmax(sentiment))
My question contains 2 parts: I wanna know is it true to predict in such way? AS far as I told I considered label 2 for neutral tweets and for this reason I considered if (np.argmax(sentiment) ==2) then print neutral - Is this logical or acceptable for prediction??
I mean I considered to assign [0.1, 1.0] for neutral tweets in train and test set so If I consider 2 as neutral in prediction part, does it make any sense??
thanks a lot
****for regression is it true to change my train and test code in such way? considering 0,1,2 as polarities in my corpus
if i < train_size:
if labels[index] == 0 :
Y_train[i, :] = [1.0, 0.0]
elif labels[index] == 1 :
Y_train[i, :] = [0.0, 1.0]
elif labels[index]==2
Y_train[i, :] = [0.5, 0.5]
else:
if labels[index] == 0 :
Y_test[i - train_size, :] = [1.0, 0.0]
elif labels[index] == 1 :
Y_test[i - train_size, :] = [0.0, 1.0]
else:
Y_test[i - train_size, :] = [0.5, 0.5]
then setting'sigmoid' for activation:
model.add(Dense(256, activation='tanh'))
model.add(Dense(256, activation='tanh'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))
and Can I predict my input tweet in the way i mentioned above??
if (np.argmax(sentiment) ==2):
print("neutral")
print('the label is')
print(np.argmax(sentiment))
*****If I used word2vec for embedding and considering 0,1,2 as polarities in my corpus Can I set labels in such way?
if i < train_size:
if labels[index] == 0 :
Y_train[i, :] = [1.0, 0.0,0.0]
elif labels[index] == 1 :
Y_train[i, :] = [0.0, 1.0,0.0]
else:
Y_train[i, :] = [0.0, 0.0,1.0]
else:
if labels[index] == 0 :
Y_test[i - train_size, :] = [1.0, 0.0,0.0]
elif labels[index] == 1 :
Y_test[i - train_size, :] = [0.0, 1.0,0.0]
else:
Y_test[i - train_size, :] = [0.0, 0.0,1.0]
and then for compiling:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
thank you for your patience
I would not recommend labeling your data in this way as defining your loss function to properly incentivize learning will be very difficult. The following would be more standard approaches.
1. Classification
label your data as it already is: simple 0,1,2. You can either label these as one-hot vectors:
[1., 0., 0.] # liberal
[0., 1., 0.] # neutral
[0., 0., 1.] # conservative
and use the categorical_crossentropy loss function, or simply pass in your labels as 0, 1, or 2 and use the sparse_categorical_crossentropy loss function. Both should behave the same way. Either way, use 3 neurons on your output layer with softmax activation.
2. Regression
Treat labeling the sentiment as a regression task from 0 to 1 with 0 being on the left, 1 being on the right, and 0.5. This way your model will train to predict the political ideology of the tweet on a continuous basis - which may be interesting to you. If you go this route use one output neuron with sigmoid activation.
Also, if it is of any help, I did political sentiment for a class project. I used RNNs though instead of 1D convolutions - here is the code anyway.