__author__ = 'arathi'
import csv
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import BernoulliNB
from sklearn import cross_validation
from sklearn.metrics import classification_report
import numpy as np
from sklearn.metrics import accuracy_score
# review.csv contains two columns
# first column is the review content (quoted)
# second column is the assigned sentiment (positive or negative)
def load_file():
with open('review.csv') as csv_file:
reader = csv.reader(csv_file,delimiter=",",quotechar='"')
reader.next()
data =[]
target = []
for row in reader:
# skip missing data
if row[0] and row[1]:
data.append(row[0])
target.append(row[1])
return data,target
# preprocess creates the term frequency matrix for the review data set
def preprocess():
data,target = load_file()
count_vectorizer = CountVectorizer(binary='true')
data = count_vectorizer.fit_transform(data)
tfidf_data = TfidfTransformer(use_idf=False).fit_transform(data)
return tfidf_data
def learn_model(data,target):
# preparing data for split validation. 60% training, 40% test
data_train,data_test,target_train,target_test = cross_validation.train_test_split(data,target,test_size=0.4,random_state=43)
classifier = BernoulliNB().fit(data_train,target_train)
predicted = classifier.predict(data_test)
evaluate_model(target_test,predicted)
# read more about model evaluation metrics here
# http://scikit-learn.org/stable/modules/model_evaluation.html
def evaluate_model(target_true,target_predicted):
print classification_report(target_true,target_predicted)
print "The accuracy score is {:.2%}".format(accuracy_score(target_true,target_predicted))
def main():
data,target = load_file()
tf_idf = preprocess()
learn_model(tf_idf,target)
main()
