Sentiment analysis with TFLearn¶

In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with Numpy, we'll be using TFLearn, a high-level library built on top of TensorFlow. TFLearn makes it simpler to build networks just by defining the layers. It takes care of most of the details for you.

We'll start off by importing all the modules we'll need, then load and prepare the data.

Preparing the data¶

Following along with Andrew, our goal here is to convert our reviews into word vectors. The word vectors will have elements representing words in the total vocabulary. If the second position represents the word 'the', for each review we'll count up the number of times 'the' appears in the text and set the second position to that count. I'll show you examples as we build the input data from the reviews data. Check out Andrew's notebook and video for more about this.

Read the data¶

Use the pandas library to read the reviews and postive/negative labels from comma-separated files. The data we're using has already been preprocessed a bit and we know it uses only lower case characters. If we were working from raw data, where we didn't know it was all lower case, we would want to add a step here to convert it. That's so we treat different variations of the same word, like The, the, and THE, all the same way.

Counting word frequency¶

To start off we'll need to count how often each word appears in the data. We'll use this count to create a vocabulary we'll use to encode the review data. This resulting count is known as a bag of words. We'll use it to select our vocabulary and build the word vectors. You should have seen how to do this in Andrew's lesson. Try to implement it here using the Counter class.

Exercise: Create the bag of words from the reviews data and assign it to total_counts. The reviews are stores in the reviews Pandas DataFrame. If you want the reviews as a Numpy array, use reviews.values. You can iterate through the rows in the DataFrame with for idx, row in reviews.iterrows(): (documentation). When you break up the reviews into words, use .split(' ') instead of .split() so your results match ours.

Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in the vocabulary are rarely used so they will have little effect on our predictions. Below, we'll sort vocab by the count value and keep the 10000 most frequent words.

What's the last word in our vocabulary? We can use this to judge if 10000 is too few. If the last word is pretty common, we probably need to keep more words.

The last word in our vocabulary shows up in 30 reviews out of 25000. I think it's fair to say this is a tiny proportion of reviews. We are probably fine with this number of words.

Note: When you run, you may see a different word from the one shown above, but it will also have the value 30. That's because there are many words tied for that number of counts, and the Counter class does not guarantee which one will be returned in the case of a tie.

Now for each review in the data, we'll make a word vector. First we need to make a mapping of word to index, pretty easy to do with a dictionary comprehension.

Exercise: Create a dictionary called word2idx that maps each word in the vocabulary to an index. The first word in vocab has index 0, the second word has index 1, and so on.

Text to vector function¶

Now we can write a function that converts a some text to a word vector. The function will take a string of words as input and return a vector with the words counted up. Here's the general algorithm to do this:

• Initialize the word vector with np.zeros, it should be the length of the vocabulary.
• Split the input string of text into a list of words with .split(' '). Again, if you call .split() instead, you'll get slightly different results than what we show here.
• For each word in that list, increment the element in the index associated with that word, which you get from word2idx.

Note: Since all words aren't in the vocab dictionary, you'll get a key error if you run into one of those words. You can use the .get method of the word2idx dictionary to specify a default returned value when you make a key error. For example, word2idx.get(word, None) returns None if word doesn't exist in the dictionary.

If you do this right, the following code should return

text_to_vector('The tea is for a party to celebrate '
               'the movie so she has no time for a cake')[:65]

array([0, 1, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0])

Now, run through our entire review data set and convert each review to a word vector.

Train, Validation, Test sets¶

Now that we have the word_vectors, we're ready to split our data into train, validation, and test sets. Remember that we train on the train data, use the validation data to set the hyperparameters, and at the very end measure the network performance on the test data. Here we're using the function to_categorical from TFLearn to reshape the target data so that we'll have two output units and can classify with a softmax activation function. We actually won't be creating the validation set here, TFLearn will do that for us later.

Building the network¶

TFLearn lets you build the network by defining the layers.

Input layer¶

For the input layer, you just need to tell it how many units you have. For example,

net = tflearn.input_data([None, 100])

would create a network with 100 input units. The first element in the list, None in this case, sets the batch size. Setting it to None here leaves it at the default batch size.

The number of inputs to your network needs to match the size of your data. For this example, we're using 10000 element long vectors to encode our input data, so we need 10000 input units.

Adding layers¶

To add new hidden layers, you use

net = tflearn.fully_connected(net, n_units, activation='ReLU')

This adds a fully connected layer where every unit in the previous layer is connected to every unit in this layer. The first argument net is the network you created in the tflearn.input_data call. It's telling the network to use the output of the previous layer as the input to this layer. You can set the number of units in the layer with n_units, and set the activation function with the activation keyword. You can keep adding layers to your network by repeated calling net = tflearn.fully_connected(net, n_units).

Output layer¶

The last layer you add is used as the output layer. Therefore, you need to set the number of units to match the target data. In this case we are predicting two classes, positive or negative sentiment. You also need to set the activation function so it's appropriate for your model. Again, we're trying to predict if some input data belongs to one of two classes, so we should use softmax.

net = tflearn.fully_connected(net, 2, activation='softmax')

Training¶

To set how you train the network, use

net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')

Again, this is passing in the network you've been building. The keywords:

• optimizer sets the training method, here stochastic gradient descent
• learning_rate is the learning rate
• loss determines how the network error is calculated. In this example, with the categorical cross-entropy.

Finally you put all this together to create the model with tflearn.DNN(net). So it ends up looking something like

net = tflearn.input_data([None, 10])                          # Input
net = tflearn.fully_connected(net, 5, activation='ReLU')      # Hidden
net = tflearn.fully_connected(net, 2, activation='softmax')   # Output
net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')
model = tflearn.DNN(net)

Exercise: Below in the build_model() function, you'll put together the network using TFLearn. You get to choose how many layers to use, how many hidden units, etc.

Intializing the model¶

Next we need to call the build_model() function to actually build the model. In my solution I haven't included any arguments to the function, but you can add arguments so you can change parameters in the model if you want.

Note: You might get a bunch of warnings here. TFLearn uses a lot of deprecated code in TensorFlow. Hopefully it gets updated to the new TensorFlow version soon.

Training the network¶

Now that we've constructed the network, saved as the variable model, we can fit it to the data. Here we use the model.fit method. You pass in the training features trainX and the training targets trainY. Below I set validation_set=0.1 which reserves 10% of the data set as the validation set. You can also set the batch size and number of epochs with the batch_size and n_epoch keywords, respectively. Below is the code to fit our the network to our word vectors.

You can rerun model.fit to train the network further if you think you can increase the validation accuracy. Remember, all hyperparameter adjustments must be done using the validation set. Only use the test set after you're completely done training the network.

Testing¶

After you're satisified with your hyperparameters, you can run the network on the test set to measure its performance. Remember, only do this after finalizing the hyperparameters.

Try out your own text!¶