def sentiment(comments):
if not os.path.isfile('classifier.pickle'):
tcl.training()
fl = open('classifier.pickle', 'rb')
classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
for words in comments:
comment = features(words)
sentiment_value, confidence = VoteClassifier(classifier).classify(
comment)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
else:
neg += 1
print("Positive sentiment : ", (pos * 100.0 / len(comments)), "%")
print("Negative sentiment : ", (neg * 100.0 / len(comments)), "%")
a = (pos * 100.0 / len(comments))
b = (neg * 100.0 / len(comments))
sys.stdout.flush()
import matplotlib.pyplot as plt
plt.figure(figsize=[5, 5])
labels = ["positive", "negative"]
values = [a, b]
plt.pie(values, labels=labels)
plt.show()
def sentiment(comments):
if not os.path.isfile('classifier.pickle'):
tcl.training()
fl = open('classifier.pickle', 'rb')
classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
for words in comments:
comment = features(words)
sentiment_value, confidence = VoteClassifier(classifier).classify(
comment)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
userpositive = open("userpositive.txt", "a", encoding="utf-8")
x = words.split(":", 1)
userpositive.write(x[0] + "\n")
userpositive.close()
else:
neg += 1
usernegative = open("usernegative.txt", "a", encoding="utf-8")
y = words.split(":", 1)
usernegative.write(y[0] + "\n")
usernegative.close()
print("Positive sentiment : ", (pos * 100.0 / len(comments)))
print("Negative sentiment : ", (neg * 100.0 / len(comments)))
def sentiment(comments):
if not os.path.isfile('/home/faizollah/mysite/classifier.pickle'):
tcl.training()
fl = open('/home/faizollah/mysite/classifier.pickle', 'rb')
classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
for words in comments:
comment = features(words)
sentiment_value, confidence = VoteClassifier(classifier).classify(
comment)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
else:
neg += 1
#print ("Positive sentiment : ", (pos * 100.0 /len(comments)) )
#print ("Negative sentiment : ", (neg * 100.0 /len(comments)) )
psent = (pos * 100.0 / len(comments))
nsent = (neg * 100.0 / len(comments))
return psent, nsent
def sentiment(comments):
if not os.path.isfile('classifier.pickle'):
tcl.training()
fl = open('classifier.pickle','rb')
classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
for words in comments:
comment = features(words)
sentiment_value, confidence = VoteClassifier(classifier).classify(comment)
df1['Sentiment']=sentiment_value
if sentiment_value == 'positive':# and confidence * 100 >= 60:
pos += 1
else:
neg += 1
def sentiment(comments, language):
if language == 0 and not os.path.isfile('chinese_classifier.pickle'):
tcl.chinese_training()
if language == 1 and not os.path.isfile('naive_bayes.pickle'):
tcl.training()
if language == 1:
fl = open('naive_bayes.pickle', 'rb')
else:
fl = open('chinese_classifier.pickle', 'rb')
naive_bayes_classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
count = 0
for words in comments:
count += 1
if language == 1:
comment = features(words)
else:
comment = chinese_features(words)
voted_classifier = VoteClassifier(naive_bayes_classifier)
sentiment_value, confidence = voted_classifier.classify(comment)
#if count <= 20:
#print(count, sentiment_value, confidence)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
else:
neg += 1
output = []
output.append("Positive sentiment : " +
str(int(pos * 100.0 / len(comments))))
output.append("Negative sentiment : " +
str(int(neg * 100.0 / len(comments))))
return output
def sentiment(comments):
if not os.path.isfile('classifier.pickle'):
tcl.training()
fl = open('classifier.pickle', 'rb')
classifier = pickle.load(fl)
fl.close()
pos = 0
neg = 0
for words in comments:
# print(words)
comment = features(words)
sentiment_value, confidence = VoteClassifier(classifier).classify(
comment)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
else:
neg += 1
print("\nPositive sentiment : ", (pos * 100.0 / len(comments)))
print("\nNegative sentiment : ", (neg * 100.0 / len(comments)))
def sentiment(comments):
if not os.path.isfile('CommentSentimentData/classifier.pkl'):
tcl.training()
fl = open('CommentSentimentData/classifier.pkl', 'rb')
classifier = pickle.load(fl)
fl.close()
with open('CommentSentimentData/bestwords.pkl', 'rb') as f:
best_words = pickle.load(f)
pos = 0
neg = 0
for words in comments:
comment = features(words, best_words)
sentiment_value, confidence = VoteClassifier(classifier).classify(
comment)
if sentiment_value == 'positive': # and confidence * 100 >= 60:
pos += 1
else:
neg += 1
print("Positive sentiment : ", (pos * 100.0 / len(comments)))
print("Negative sentiment : ", (neg * 100.0 / len(comments)))