Python TextBlob.classify方法代码示例

# 需要导入模块: from textblob import TextBlob [as 别名]
# 或者: from textblob.TextBlob import classify [as 别名]
def find_sentiment(string):

    # train the classifier
    cl = NaiveBayesClassifier(train)

    # load the classifier
    blob = TextBlob(string, classifier=cl)

    print "\n\n\n\t\t\t{}".format(blob.classify())

    return

# 需要导入模块: from textblob import TextBlob [as 别名]
# 或者: from textblob.TextBlob import classify [as 别名]
def analyze_tweets(cl):

    file = 'consumer.json'
    f = open(file, 'r')
    data = f.read()
    blob = TextBlob(data,classifier=cl)

    a = blob.classify()

    print 'The product have an overall ' + a + ' rating\n'
    #print 'The majority of the tweets that were positive are ' + str(b) + '\n'
    #print 'The percent of the tweets that were negative are ' + str(c) + '\n'
    return 'The product have an overall ' + a + ' rating\n'

# 需要导入模块: from textblob import TextBlob [as 别名]
# 或者: from textblob.TextBlob import classify [as 别名]
    ('My boss is horrible.', 'neg')]

test = [
	('The beer was good.', 'pos'),
    ('I do not enjoy my job', 'neg'),
    ("I ain't feeling dandy today.", 'neg'),
    ("I feel amazing!", 'pos'),
    ('Gary is a friend of mine.', 'pos'),
    ("I can't believe I'm doing this.", 'neg')]

cl = NaiveBayesClassifier(train)

# Classify some text
# print(cl.classify("Their burgers are amazing."))  # "pos"
# print(cl.classify("I don't like their pizza."))   # "neg"

# Classify a TextBlob
blob = TextBlob("The beer was amazing. But the hangover was horrible. "
                "My boss was not pleased.", classifier=cl)
print(blob)
print(blob.classify())

for sentence in blob.sentences:
    print(sentence)
    print(sentence.classify())

# Compute accuracy
print("Accuracy: {0}".format(cl.accuracy(test)))

# Show 5 most informative features
cl.show_informative_features(5)

# 需要导入模块: from textblob import TextBlob [as 别名]
# 或者: from textblob.TextBlob import classify [as 别名]
cl = NaiveBayesClassifier(train)
print cl.classify(tx_cl)
print cl.classify("El subte funciona bien")
prob_dist = cl.prob_classify(tx_prob)
print prob_dist.max()
print round(prob_dist.prob("pos"), 2)
print round(prob_dist.prob("neg"), 2)

print cl.accuracy(data_sets.en_test)
print cl.show_informative_features(5)

#Using TextBlob
blob = TextBlob("No funca por que hay obras para mejorar la cosa", classifier=cl)
print blob.sentiment
print blob.classify()

blob = TextBlob("El subte funciona normal", classifier=cl)
print blob.sentiment
print blob.classify()

blob = TextBlob("Se realizan obras en el subte A", classifier=cl)
print blob.sentiment
print blob.classify()

blob = TextBlob("No funciona, anda averiguar por que. Quizas hay un accidente", classifier=cl)
print blob.sentiment
print blob.classify()

blob = TextBlob(u"El subte funciona ok", classifier=cl)
print blob.sentiment

# 需要导入模块: from textblob import TextBlob [as 别名]
# 或者: from textblob.TextBlob import classify [as 别名]
 def testSentence(self,sentence,classifier):
     filtered_word_list = [word for word in sentence.lower().split() if word not in stopwords.words('english')]
     refinedText = " ".join(filtered_word_list)
     blob = TextBlob(refinedText, classifier=classifier)
     return blob.classify()