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dep2.py
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dep2.py
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# -*- coding: utf-8 -*-
"""
Created on Wed May 15 22:04:22 2019
@author: POOJA
"""
#import nltk
#nltk.download('punkt')
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import pickle
from math import log
import csv
import io
import gethistory
def process_text(text, lower_case = True, stem = True, stop_words = True, gram = 2):
if lower_case:
text = text.lower()
words = word_tokenize(text)
words = [w for w in words if len(w) > 2]
if gram > 1:
w = []
for i in range(len(words) - gram + 1):
w += [' '.join(words[i:i + gram])]
return w
if stop_words:
sw = stopwords.words('english')
words = [word for word in words if word not in sw]
if stem:
stemmer = PorterStemmer()
words = [stemmer.stem(word) for word in words]
return words
class Classifier(object):
def __init__(self, trainData):
self.text, self.labels = trainData['text'], trainData['label']
def train(self):
self.calc_TF_and_IDF()
self.calc_TF_IDF()
def calc_TF_and_IDF(self):
noOftext = self.text.shape[0]
self.depressive_text, self.positive_text = self.labels.value_counts()[1], self.labels.value_counts()[0]
self.total_text = self.depressive_text + self.positive_text
self.depressive_words = 0
self.positive_words = 0
self.tf_depressive = dict()
self.tf_positive = dict()
self.idf_depressive = dict()
self.idf_positive = dict()
for i in range(noOftext):
text_processed = process_text(self.text.iloc[i])
count = list()
for word in text_processed:
if self.labels.iloc[i]:
self.tf_depressive[word] = self.tf_depressive.get(word, 0) + 1
self.depressive_words += 1
else:
self.tf_positive[word] = self.tf_positive.get(word, 0) + 1
self.positive_words += 1
if word not in count:
count += [word]
for word in count:
if self.labels.iloc[i]:
self.idf_depressive[word] = self.idf_depressive.get(word, 0) + 1
else:
self.idf_positive[word] = self.idf_positive.get(word, 0) + 1
def calc_TF_IDF(self):
self.prob_depressive = dict()
self.prob_positive = dict()
self.sum_tf_idf_depressive = 0
self.sum_tf_idf_positive = 0
for word in self.tf_depressive:
self.prob_depressive[word] = (self.tf_depressive[word]) * log((self.depressive_text + self.positive_text) \
/ (self.idf_depressive[word] + self.idf_positive.get(word, 0)))
self.sum_tf_idf_depressive += self.prob_depressive[word]
for word in self.tf_depressive:
self.prob_depressive[word] = (self.prob_depressive[word] + 1) / (self.sum_tf_idf_depressive + len(list(self.prob_depressive.keys())))
for word in self.tf_positive:
self.prob_positive[word] = (self.tf_positive[word]) * log((self.depressive_text + self.positive_text) \
/ (self.idf_depressive.get(word, 0) + self.idf_positive[word]))
self.sum_tf_idf_positive += self.prob_positive[word]
for word in self.tf_positive:
self.prob_positive[word] = (self.prob_positive[word] + 1) / (self.sum_tf_idf_positive + len(list(self.prob_positive.keys())))
self.prob_depressive_text, self.prob_positive_text = self.depressive_text / self.total_text, self.positive_text / self.total_text
def classify(self, processed_text):
pDepressive, pPositive = 0, 0
for word in processed_text:
if word in self.prob_depressive:
pDepressive += log(self.prob_depressive[word])
else:
pDepressive -= log(self.sum_tf_idf_depressive + len(list(self.prob_depressive.keys())))
if word in self.prob_positive:
pPositive += log(self.prob_positive[word])
else:
pPositive -= log(self.sum_tf_idf_positive + len(list(self.prob_positive.keys())))
pDepressive += log(self.prob_depressive_text)
pPositive += log(self.prob_positive_text)
return pDepressive >= pPositive
def predict(self, testData):
result = dict()
for (i, text) in enumerate(testData):
processed_text = process_text(text)
result[i] = int(self.classify(processed_text))
return result
Model = pickle.load(open("finalized_model.sav", 'rb'))
with io.open("chrome_history.csv", 'r',encoding="utf8") as csvinput:
with io.open("history_prediction.csv", 'w',encoding="utf8") as csvoutput:
writer = csv.writer(csvoutput, lineterminator='\n')
reader = csv.reader(csvinput)
all = []
true = 0
false = 0
for row in reader:
text = process_text(row[1])
result= Model.classify(text)
if result:
true = true + 1
else :
false = false + 1
row.append(result)
all.append(row)
print("total depressive sentences : " , true)
print("total positive sentences : ",false)
writer.writerows(all)
#myList = [true, false]
if (true>=180):
rate=1
elif(true>=160):
rate=2
elif(true>=140):
rate=3
elif(true>=120):
rate=4
elif(true>=100):
rate=5
elif(true>=80):
rate=6
elif(true>=60):
rate=7
elif(true>=40):
rate=8
elif(true>=20):
rate=9
else :
rate=10
f= open("extension/try","w+")
f.write("Depression Rating : ")
f.write(str(rate))
f.close()
f= open("extension/p","w+")
f.write(str(true))
f.close()
csvoutput.close()
csvinput.close()