data_target = data['Label']
kfold = KFold(data_tweet, data_target, 10)
data_train, data_test = kfold.get_data_sequence()
i = 0
print("kfold")
print(time.time() - start)
start = time.time()
prepro = Preprocessing()
cleaned_data, terms, asd = prepro.preprocessing(data_train[i]["tweet"])
print("preprocessing")
print(time.time() - start)
start = time.time()
tbrs = TermBasedRandomSampling(X=10, Y=10, L=40)
stopwords = tbrs.create_stopwords(cleaned_data, terms)
print("remove stopword")
print(time.time() - start)
start = time.time()
prepro2 = Preprocessing()
new_cleaned_data, new_terms, removed_words = prepro2.remove_stopword(
cleaned_data, stopwords)
print("create stopword")
print(time.time() - start)
start = time.time()
weight = Weighting(new_cleaned_data, new_terms)
for i in range(9):
x_array.append(" ")
y_array.append(" ")
l_array.append(" ")
accuracy_total_accumulation = 0
for i in range(len(data_train)):
kfold_per_combination.append(i+1)
y_test = []
y_pred = []
prepro = Preprocessing()
cleaned_data, terms = prepro.preprocessing(data_train[i]["tweet"])
tbrs = TermBasedRandomSampling(X=x, Y=y, L=l)
stopwords = tbrs.create_stopwords(cleaned_data,terms)
prepro2 = Preprocessing()
new_cleaned_data, new_terms = prepro2.remove_stopword(cleaned_data, stopwords)
weight = Weighting(new_cleaned_data, new_terms)
tfidf = weight.get_tf_idf_weighting()
idf = weight.get_idf()
nb = NBMultinomial()
nb.fit(new_cleaned_data,new_terms,data_train[i]["target"],stopwords,idf,tfidf)
correct_ans = 0
for j in range(len(data_test[i]["tweet"])):
prediction = nb.predict(data_test[i]["tweet"][j],data_test[i]["target"][j])