Comparing performance of n-gram models
You now know how to conduct sentiment analysis by converting text into various n-gram representations and feeding them to a classifier. In this exercise, we will conduct sentiment analysis for the same movie reviews from before using two n-gram models: unigrams and n-grams upto n equal to 3.
We will then compare the performance using three criteria: accuracy of the model on the test set, time taken to execute the program and the number of features created when generating the n-gram representation.
This exercise is part of the course
Feature Engineering for NLP in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
start_time = time.time()
# Splitting the data into training and test sets
train_X, test_X, train_y, test_y = train_test_split(df['review'], df['sentiment'], test_size=0.5, random_state=42, stratify=df['sentiment'])
# Generating ngrams
vectorizer = ___
train_X = vectorizer.fit_transform(train_X)
test_X = vectorizer.transform(test_X)
# Fit classifier
clf = MultinomialNB()
clf.fit(train_X, train_y)
# Print accuracy, time and number of dimensions
print("The program took %.3f seconds to complete. The accuracy on the test set is %.2f. The ngram representation had %i features." % (time.time() - start_time, clf.score(test_X, test_y), train_X.shape[1]))