Sentiment Analysis Analysis – Support Vector Machines

In the next set of topics we will dive into different approachs to solve the hello world problem of the NLP world, the sentiment analysis . Sentiment analysis is an area of research that aims to tell if the sentiment of a portion of text is positive or negative.

Check the other parts: Part1 Part2 Part3

The code for this implementation is at

The SVM Classifier

This classifier works trying to create a line that divides the dataset leaving the larger margin as possible between points called support vectors. As per the figure below, the line A has a larger margin than the line B, so the points divided by the line A have to travel much more to cross the division, than if the data was divided by B, so in this case we would choose the line A.

1 VqBLDxlpqL7nJkqRYuOdCw.png

The Code

For this task we will use scikit-learn, an open source machine learning library.

Our dataset is composed of movie reviews and labels telling whether the review is negative or positive. Let’s load the dataset:

The reviews file is a little big, so it is in zip format. Let’s Extract it:

import zipfile
with zipfile.ZipFile("", 'r') as zip_ref:

Now that we have the reviews.txt and labels.txt files, we load them to the memory:

with open("reviews.txt") as f:
    reviews ="\n")
with open("labels.txt") as f:
    labels ="\n")

reviews_tokens = [review.split() for review in reviews]

Next we load the module to transform our review inputs into binary vectors with the help of the class MultiLabelBinarizer :

from sklearn.preprocessing import MultiLabelBinarizer
onehot_enc = MultiLabelBinarizer()

After that we split the data into training and test set with the train_test_split function:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(reviews_tokens, labels, test_size=0.25, random_state=None)

We then create our SVM classifier with the class LinearSVC and train it:

from sklearn.svm import LinearSVC
lsvm = LinearSVC(), y_train)

Training the model took about 2 seconds.

After training, we use the score function to check the performance of the classifier:

score = lsvm.score(onehot_enc.transform(X_test), y_test)

Computing the score took about 1 second only!

Running the classifier a few times we get around 85% of accuracy, basically the same of the result of the naive bayes classifier.

If you have any questions or comments, leave them below!

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