def classify(self, dataSet):
"""
Recebe um dataSet e aplica o extrairFrase para o dataSet
:param dataSet:
:return:
"""
return nltk.classify.apply_features(self.extrairFrase, dataSet)
from database.DataBase import DataBase
db = DataBase()
dataSet=db.get_all_data_set(['vaticannews', 'semprequestione'])
# data=db.get_all_news_from('vaticannews')
p = Process(dataSet)
sp =p.stemmerAplay()
# print(p.extrairFrase())
print(type(p.extrairFrase(dataSet)))
print(type(p.freqWords(p.buscaPalavras()).values()))
# d=p.freqWords(p.buscaPalavras()).
# classificador=nltk.NaiveBayesClassifier.train(d)
# #
# print(classificador.show_most_informative_features(10))
# for s in st.most_common(50):
# print(s)
import nltk
from database.DataBase import DataBase
from sklearn.model_selection import train_test_split
from facebookapi.publushFacebook import PublishFacebook
from preprocess.Process import Process
from util.Character import removerAcentosECaracteresEspeciais
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
db = DataBase()
data = db.get_all_data_set(
['vaticannews', 'semprequestione', 'acidigital', 'cancaonova'])
y = [clazz for (title, news, clazz) in data]
X = [news for (title, news, clazz) in data]
def train(classifier, X, y):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=33)
classifier.fit(X_train, y_train)
print("Accuracy: %s" % classifier.score(X_test, y_test))
return classifier