def main():
# reading data
print("reading data")
data = pd.read_json('../Dataset/100/Data.json' , encoding="utf8")
# cleaning and parsing data
print("cleaning and parsing data \n")
cleanData = []
for i in range(0,len(data)):
cleanData.append(preprocessing.postToWord(data["text"][i]))
if (i % 1000 == 0):
print("Post %d of %d...\n" % (i, len(data)))
# print(cleanTrain[i])
# top unigrams before removing stop words
common_words = get_top_n_words(data['text'], 20 , 1)
df1 = pd.DataFrame(common_words, columns = ['ReviewText' , 'count'])
df1['ReviewText'] = [persianEncoding(df1['ReviewText'][i]) for i in range(0 , len(df1['ReviewText']))]
plotBarChart(tuple(list(df1['ReviewText'])) , list(df1['count']) , "top unigrams before removing stop words" , "Words" , "Counts" )
# top unigrams after removing stop words
common_words = get_top_n_words(cleanData, 40 , 1)
df2 = pd.DataFrame(common_words, columns = ['ReviewText' , 'count'])
df2['ReviewText'] = [persianEncoding(df2['ReviewText'][i]) for i in range(0 , len(df2['ReviewText']))]
plotBarChart(tuple(list(df2['ReviewText'])) , list(df2['count']) , "top unigrams after removing stop words" , "Words" , "Counts" )
# top bigrams before removing stop words
common_words = get_top_n_words(cleanData, 20 , 2)
df3 = pd.DataFrame(common_words, columns = ['ReviewText' , 'count'])
df3['ReviewText'] = [persianEncoding(df3['ReviewText'][i]) for i in range(0 , len(df3['ReviewText']))]
plotBarChart(tuple(list(df3['ReviewText'])) , list(df3['count']) , "top bigrams before removing stop words" , "Words" , "Counts" )
# top bigrams after removing stop words
common_words = get_top_n_words(cleanData, 20 , 2)
df4 = pd.DataFrame(common_words, columns = ['ReviewText' , 'count'])
df4['ReviewText'] = [persianEncoding(df4['ReviewText'][i]) for i in range(0 , len(df4['ReviewText']))]
plotBarChart(tuple(list(df4['ReviewText'])) , list(df4['count']) , "top bigrams after removing stop words" , "Words" , "Counts" )
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
# from sklearn.cross_validation import cross_val_score, cross_val_predict
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from time import time
print("Cleaning and parsing telegram post...\n")
cleanData = []
Data = pd.read_json('../DataSet/100/Data.json', encoding="utf8")
for i in range(0, len(Data)):
cleanData.append(preprocessing.postToWord(Data["text"][i]))
if (i % 1000 == 0):
print("Post %d of %d...\n" % (i, len(Data)))
print("**************************")
print("cleanData Ok...")
print("**************************")
# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.
vectorizer = CountVectorizer(analyzer="word",
tokenizer=None,
preprocessor=None,
stop_words=None,
max_features=5000)
# vectorizer = CountVectorizer(analyzer = "word",tokenizer = None,preprocessor = None,stop_words = None,max_features = 5000 , ngram_range=(2,2))
from time import time
from sklearn.feature_extraction.text import CountVectorizer , TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
print("Cleaning and parsing the training set telegram post...\n")
cleanTrain = []
train = pd.read_json('../DataSet/80_20/LabeledTrainedData.json' , encoding="utf8")
for i in range(0,len(train)):
cleanTrain.append(preprocessing.postToWord(train["text"][i]))
if (i % 1000 == 0):
print("Post %d of %d...\n" % (i, len(train)))
# print(cleanTrain[i])
print("**************************")
print("cleanTrain Ok...")
print("**************************")
# Initialize the "CountVectorizer" object, which is scikit-learn's
# # bag of words tool.
# vectorizer = CountVectorizer(analyzer = "word",tokenizer = None,preprocessor = None,stop_words = None,max_features = 5000)
# vectorizer = CountVectorizer(analyzer = "word",tokenizer = None,preprocessor = None,stop_words = None,max_features = 5000 , ngram_range=(2,2))
# And now testing TFIDF vectorizer:
vectorizer = TfidfVectorizer(analyzer = "word",tokenizer = None,preprocessor = None,stop_words = None,max_features = 5000)
tokenizer=None,
preprocessor=None,
stop_words=None,
max_features=5000)
# threshold for z-scor
threshold = 30
politicalData = Data[Data['class'] != 0]
cleanPolitical = []
# cleaning political data
print("cleaning political data\n")
for index in Data.index[Data['class'] != 0].tolist():
cleanPolitical.append(
preprocessing.postToWord(politicalData["text"][index]))
if (index % 1000 == 0):
print("Post %d ...\n" % (index))
# transform political data into feature vectors
politicalDataFeatures = vectorizer.fit_transform(cleanPolitical)
# convert the result to an Numpy array
politicalDataFeatures = politicalDataFeatures.toarray()
print("political data befor removing outlier: ", politicalDataFeatures.shape,
"\n")
post_df = pd.DataFrame(politicalDataFeatures)
z = np.abs(stats.zscore(post_df))
political_o = post_df[(z < threshold).all(axis=1)]
