def classify(title="NAN", text="NAN"):
data = feature2.process(text)
data = pd.DataFrame(data)
data.columns = data.columns = [
'text_wordCount', 'text_charCount', 'text_puncCount',
'text_upperCount', 'text_gunning_fog',
'text_automated_readability_index', 'text_linsear_write_formula',
'text_difficult_words', 'text_dale_chall_readability_score', 'txp1',
'txp2', 'txp3', 'txp4', 'txp5', 'txp6', 'txp7', 'txp8', 'txp9', 'txp10'
]
data1 = feature2.process(title)
data1 = pd.DataFrame(data1)
data1.columns = [
'title_wordCount', 'title_charCount', 'title_puncCount',
'title_upperCount', 'title_gunning_fog',
'title_automated_readability_index', 'title_linsear_write_formula',
'title_difficult_words', 'title_dale_chall_readability_score', 'tlp1',
'tlp2', 'tlp3', 'tlp4', 'tlp5', 'tlp6', 'tlp7', 'tlp8', 'tlp9', 'tlp10'
]
result = pd.concat([data, data1], axis=1, join='inner')
file_Name = "trainedModel.sav"
fileObject = open(file_Name, 'rb')
# load the object from the file into var b
b = pickle.load(fileObject)
a = b.predict(result)
result = result.values
#print(result[1])
return a, result
def classify(title="NAN", text="NAN"):
data = feature2.process(text)
data = pd.DataFrame(data)
data.columns = data.columns = [
'text_wordCount', 'text_charCount', 'text_puncCount',
'text_upperCount', 'text_gunning_fog',
'text_automated_readability_index', 'text_linsear_write_formula',
'text_difficult_words', 'text_dale_chall_readability_score', 'txp1',
'txp2', 'txp3', 'txp4', 'txp5', 'txp6', 'txp7', 'txp8', 'txp9', 'txp10'
]
data1 = feature2.process(title)
data1 = pd.DataFrame(data1)
data1.columns = [
'title_wordCount', 'title_charCount', 'title_puncCount',
'title_upperCount', 'title_gunning_fog',
'title_automated_readability_index', 'title_linsear_write_formula',
'title_difficult_words', 'title_dale_chall_readability_score', 'tlp1',
'tlp2', 'tlp3', 'tlp4', 'tlp5', 'tlp6', 'tlp7', 'tlp8', 'tlp9', 'tlp10'
]
result = pd.concat([data, data1], axis=1, join='inner')
file_Name1 = "trainedModel.sav"
fileObject1 = open(file_Name1, 'rb')
file_Name2 = "svm.sav"
fileObject2 = open(file_Name2, 'rb')
# load the object from the file into var b
rf = pickle.load(fileObject1)
svm = pickle.load(fileObject2)
a1 = rf.predict(result)
a2 = svm.predict(result)
if a1[0] == 1 and a2[0] == 0:
a = 'Authentic News'
elif a1[0] == 0 and a2[0] == 0:
a = 'Fake News'
else:
a = 'Authentic News'
# if a2[0] == 0:
# a = 'Fake News'
#
# else:
# a = 'Authentic News'
result = result.values
# print(result[1])
a = b.predict(result)
print(a)