test_features = vectorizer.transform([r for r in test[0]])
# Fit a naive bayes model to the training data.
# This will train the model using the word counts we computer,
# and the existing classifications in the training set.
nb = MultinomialNB()
nb.fit(train_features, [int(r) for r in reviews[1]])
# Now we can use the model to predict classifications for our test features.
predictions = nb.predict(test_features)
# Compute the error.
print(metrics.classification_report(test[1], predictions))
print("accuracy: {0}".format(metrics.accuracy_score(test[1], predictions)))
while True:
sentences = []
sentence = raw_input("\n\033[93mPlease enter a sentence to get sentiment evaluated. Enter \"exit\" to quit.\033[0m\n")
if sentence == "exit":
print("\033[93mexit program ...\033[0m\n")
break
else:
sentences.append(sentence)
input_features = vectorizer.transform(extract_words(sentences))
prediction = nb.predict(input_features)
if prediction[0] == 1 :
print("---- \033[92mpositive\033[0m\n")
else:
print("---- \033[91mneagtive\033[0m\n")
test_features = vectorizer.transform([r for r in test[0]])
# Fit a naive bayes model to the training data.
# This will train the model using the word counts we computer,
# and the existing classifications in the training set.
nb = MultinomialNB()
nb.fit(train_features, [int(r) for r in reviews[1]])
# Now we can use the model to predict classifications for our test features.
predictions = nb.predict(test_features)
# Compute the error.
print(metrics.classification_report(test[1], predictions))
print("accuracy: {0}".format(metrics.accuracy_score(test[1], predictions)))
while True:
sentences = []
sentence = input("\n\033[93mPlease enter a sentence to get sentiment evaluated. Enter \"exit\" to quit.\033[0m\n")
if sentence == "exit":
print("\033[93mexit program ...\033[0m\n")
break
else:
sentences.append(sentence)
input_features = vectorizer.transform(extract_words(sentences))
prediction = nb.predict(input_features)
if prediction[0] == 1 :
print("---- \033[92mpositive\033[0m\n")
else:
print("---- \033[91mneagtive\033[0m\n")