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Sentiment analysis with scikit-learn
# You need to install scikit-learn:
# sudo pip install scikit-learn
#
# Dataset: Polarity dataset v2.0
# http://www.cs.cornell.edu/people/pabo/movie-review-data/
#
# Full discussion:
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn
import sys
import os
import time
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.metrics import classification_report
def usage():
print("Usage:")
print("python %s <data_dir>" % sys.argv[0])
if __name__ == '__main__':
if len(sys.argv) < 2:
usage()
sys.exit(1)
data_dir = sys.argv[1]
classes = ['pos', 'neg']
# Read the data
train_data = []
train_labels = []
test_data = []
test_labels = []
for curr_class in classes:
dirname = os.path.join(data_dir, curr_class)
for fname in os.listdir(dirname):
with open(os.path.join(dirname, fname), 'r') as f:
content = f.read()
if fname.startswith('cv9'):
test_data.append(content)
test_labels.append(curr_class)
else:
train_data.append(content)
train_labels.append(curr_class)
# Create feature vectors
vectorizer = TfidfVectorizer(min_df=5,
max_df = 0.8,
sublinear_tf=True,
use_idf=True)
train_vectors = vectorizer.fit_transform(train_data)
test_vectors = vectorizer.transform(test_data)
# Perform classification with SVM, kernel=rbf
classifier_rbf = svm.SVC()
t0 = time.time()
classifier_rbf.fit(train_vectors, train_labels)
t1 = time.time()
prediction_rbf = classifier_rbf.predict(test_vectors)
t2 = time.time()
time_rbf_train = t1-t0
time_rbf_predict = t2-t1
# Perform classification with SVM, kernel=linear
classifier_linear = svm.SVC(kernel='linear')
t0 = time.time()
classifier_linear.fit(train_vectors, train_labels)
t1 = time.time()
prediction_linear = classifier_linear.predict(test_vectors)
t2 = time.time()
time_linear_train = t1-t0
time_linear_predict = t2-t1
# Perform classification with SVM, kernel=linear
classifier_liblinear = svm.LinearSVC()
t0 = time.time()
classifier_liblinear.fit(train_vectors, train_labels)
t1 = time.time()
prediction_liblinear = classifier_liblinear.predict(test_vectors)
t2 = time.time()
time_liblinear_train = t1-t0
time_liblinear_predict = t2-t1
# Print results in a nice table
print("Results for SVC(kernel=rbf)")
print("Training time: %fs; Prediction time: %fs" % (time_rbf_train, time_rbf_predict))
print(classification_report(test_labels, prediction_rbf))
print("Results for SVC(kernel=linear)")
print("Training time: %fs; Prediction time: %fs" % (time_linear_train, time_linear_predict))
print(classification_report(test_labels, prediction_linear))
print("Results for LinearSVC()")
print("Training time: %fs; Prediction time: %fs" % (time_liblinear_train, time_liblinear_predict))
print(classification_report(test_labels, prediction_liblinear))
@bonzanini
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bonzanini commented Nov 9, 2015

@mk01github The code was developed and tested on Python 3 rather than 2.7, that's often a source of encoding problems

@FrendyAr
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FrendyAr commented Aug 30, 2017

capture
why i got this error ?
help me please....
thanks

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