test.py

<<<<<<< HEAD
# It will import all the modules stored in AllImport module
from AllImport import *
# Use hashtag and classify in % how many sentiments are +ve and -ve based on fetched tweets
from tkinter import *
import time


class TwitterClient():
	def __init__(self, twitter_user=None):
		self.auth = TwitterAuthenticator().authenticate_twitter_app()
		self.twitter_client = API(self.auth)
		self.twitter_user = twitter_user

	def get_twitter_client_api(self):
		return self.twitter_client

	def get_user_timeline_tweets(self, num_tweets):
		tweets = []
		for tweet in Cursor(self.twitter_client.user_timeline, id=self.twitter_user).items(num_tweets):
			tweets.append(tweet)
		return tweets

	def get_friend_list(self, num_friends):
		friend_list = []
		for friend in Cursor(self.twitter_client, id=self.twitter_user).items(num_friends):
			friend_list.append(friend)
		return friend_list

	def get_home_timeline_tweets(self, num_tweets):
		home_timeline_tweets = []
		for tweet in Cursor(self.twitter_client.home_timeline, id=self.twitter_user).items(num_tweets):
			home_timeline_tweets.append(tweet)
		return home_timeline_tweets


# To authenticate and access the twitter
class TwitterAuthenticator():

	def authenticate_twitter_app(self):
		auth = OAuthHandler(twitter_credentials.CONSUMER_KEY, twitter_credentials.CONSUMER_SECRET)
		auth.set_access_token(twitter_credentials.ACCESS_TOKEN, twitter_credentials.ACCESS_TOKEN_SECRET)
		return auth


# get all the data of the tweets and,pass only tweets text to preprocess and finally returns only the processed tweets
def process(data):
	temp = []
	for text in data['sentence']:
		text = pp.pre_processing(text)
		temp.append(text)
	data['sentence'] = temp
	return data['sentence']


def execute():
	try:
		user = Entry1.get()
		num_tweets = w.get()
		twitter_client = TwitterClient()
		api = twitter_client.get_twitter_client_api()
		tweets = api.user_timeline(screen_name=user, count=num_tweets)
		tweets_text = []
		for tweet in tweets:
			tweets_text.append(pp.pre_processing(tweet.text))
		datafile = pd.read_csv('Train.csv', sep=',', encoding="utf-8")
		x = process(datafile)
		y = datafile['label']
		x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
		vector = CountVectorizer()
		vector.fit(x_train)
		x_train_vft = vector.transform(x_train)
		x_test_vft = vector.transform(x_test)

		count = 1
		for tweet in tweets_text:
			tweet_text = str(count)+":- "+tweet
			msg_list.insert(END,tweet_text)
			# print(count, tweet)
			count += 1
			tweet = [tweet]
			vec = vector.transform(tweet)

		# Multinomial Naive Bayes-Every feature is independent,probability is cal and highest one will be o/p,fastest
			temp = mnb.MultinomialNBAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Multinomial Naive Bayes")
			msg_list.insert(END,temp)
			
			"""
		Regression analysis is an important tool for modelling and analyzing data. Here, we fit a curve/line to the data
		Points,in such a manner that the differences b/w the distances of data points from the curve/line is minimized.
			a topic of some context.Ex:context:-road accident,topic:-car accident,it can happen or not happen
			"""

			"""
			Logistic Regression-It can give a binary or multi result(positive/negative/neutral),has a range 0 to 1
			# used for category data.Its has a curve.3 Types
			# lbgfs or lmbgfs is Limited memory Broyden–Fletcher–Goldfarb–Shanno Algo.Memory optimization algo
			# newton-cg:- newton's method for Large Bound-Constrained Optimization
			# multi-calss tells which logistic regression is being used
			"""

			# 1:-OrdinalLogisticRegression not used because it takes at lest 3 categories but we have 2,+ve and -ve

			# 2:-Multinomial Logistic Regression-Used for 2 or more category,vision-shortsight,longsight,perfect
			temp = mlr.MultinomialLRAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Multinomial Logistic Regression")
			msg_list.insert(END,temp)
			
			# 3:-Binary logistic regression-Used for 2 category,good,bad
			temp = blr.BinomialLRAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Binary logistic regression")
			msg_list.insert(END,temp)
			
		# LinearRegression-find optimal line b/w the 2 data,where one data is independent(text),and other is dependent
			# (type-pos/neg) on another
			# lr.LinearRegressionAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			"""
		SVM(support vector machine)-takes data as i/p and o/p a line that separates those classes[pos/neg] if possible
		we find the points closest to the line from both the classes.These points are called support vectors.we compute 
			the distance between the line and the support vectors. This distance is called the margin. Our goal is to 
		maximize the margin. The hyperplane for which the margin is maximum is the optimal hyperplane.Thus SVM tries to
		make a decision boundary in such a way that the separation between the two classes(that street) is as wide as 
			possible
			"""

			# Linear Classifier
			temp = lc.LinearClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Linear Classifier")
			msg_list.insert(END,temp)
			
			# LinearSupportVectorClassifier-LinearSeparationOfDataHappensOptimalLineIsDrawn using margins b/w both data
			temp = lsvc.LinearSupportVectorClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"LinearSupportVectorClassifier")
			msg_list.insert(END,temp)
			
			# Decision Tree Classifier
			temp = dtc.DecisionTreeClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Decision Tree Classifier")
			msg_list.insert(END,temp)

			# Random Forest classifier
			temp = rfc.RandomForestClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Random Forest classifier")
			msg_list.insert(END,temp)

			# Extra Trees Classifier
			temp = etc.ExtraTreesClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Extra Trees Classifier")
			msg_list.insert(END,temp)
			msg_list.insert(END," ")

	except Exception as e:
		# Print the error
		print(e)

		# When reach the rate limit
		def on_limit(self, track):
			# Print rate limiting error
			print("Rate limited, continuing")
			# Continue mining tweets
			return True
		# When timed out

		def on_timeout(self):
			# Print timeout message
			print(sys.stderr, 'Timeout')
			# Wait 10 seconds
			time.sleep(10)
			# Return nothing
			return

if __name__ == "__main__":
	mainwindow = Tk()
	mainwindow.title("Twitter Sentimental Analysis Engine")
	Label(mainwindow, text="TWITTER SENTIMENTAL ANALYSIS ENGINE", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	photo = PhotoImage(file="Twitterlogo.png")
	Label(mainwindow, image=photo, bg="black", fg="white").pack(side=TOP, fill=X)
	
	messages_frame = Frame(mainwindow)
	scrollbar = Scrollbar(messages_frame)  # To navigate through past messages.
	# Following will contain the messages.
	msg_list = Listbox(messages_frame, height=15, width=50, yscrollcommand=scrollbar.set)
	scrollbar.pack(side=RIGHT, fill=Y,padx=2,pady=2)
	msg_list.pack(side=LEFT, fill=BOTH,padx=2,pady=2)
	msg_list.pack(padx=2,pady=2)
	messages_frame.pack()

	Label(mainwindow, text="USERNAME", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	Entry1 = Entry(mainwindow)
	Entry1.pack(side=TOP, padx=2, pady=2)
	Label(mainwindow, text="NUMBER OF TWEETS", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	w = Scale(mainwindow, from_=1, to=10, orient=HORIZONTAL)
	w.pack(side=TOP, fill=X, padx=2, pady=2)
	But1 = Button(mainwindow, text="RUN", command=execute)
	But1.pack(side=TOP, fill=X, padx=2, pady=2)
=======
# It will import all the modules stored in AllImport module
from AllImport import *
# Use hashtag and classify in % how many sentiments are +ve and -ve based on fetched tweets
from tkinter import *
import time


class TwitterClient():
	def __init__(self, twitter_user=None):
		self.auth = TwitterAuthenticator().authenticate_twitter_app()
		self.twitter_client = API(self.auth)
		self.twitter_user = twitter_user

	def get_twitter_client_api(self):
		return self.twitter_client

	def get_user_timeline_tweets(self, num_tweets):
		tweets = []
		for tweet in Cursor(self.twitter_client.user_timeline, id=self.twitter_user).items(num_tweets):
			tweets.append(tweet)
		return tweets

	def get_friend_list(self, num_friends):
		friend_list = []
		for friend in Cursor(self.twitter_client, id=self.twitter_user).items(num_friends):
			friend_list.append(friend)
		return friend_list

	def get_home_timeline_tweets(self, num_tweets):
		home_timeline_tweets = []
		for tweet in Cursor(self.twitter_client.home_timeline, id=self.twitter_user).items(num_tweets):
			home_timeline_tweets.append(tweet)
		return home_timeline_tweets


# To authenticate and access the twitter
class TwitterAuthenticator():

	def authenticate_twitter_app(self):
		auth = OAuthHandler(twitter_credentials.CONSUMER_KEY, twitter_credentials.CONSUMER_SECRET)
		auth.set_access_token(twitter_credentials.ACCESS_TOKEN, twitter_credentials.ACCESS_TOKEN_SECRET)
		return auth


# get all the data of the tweets and,pass only tweets text to preprocess and finally returns only the processed tweets
def process(data):
	temp = []
	for text in data['sentence']:
		text = pp.pre_processing(text)
		temp.append(text)
	data['sentence'] = temp
	return data['sentence']


def execute():
	try:
		user = Entry1.get()
		num_tweets = w.get()
		twitter_client = TwitterClient()
		api = twitter_client.get_twitter_client_api()
		tweets = api.user_timeline(screen_name=user, count=num_tweets)
		tweets_text = []
		for tweet in tweets:
			tweets_text.append(pp.pre_processing(tweet.text))
		datafile = pd.read_csv('Train.csv', sep=',', encoding="utf-8")
		x = process(datafile)
		y = datafile['label']
		x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
		vector = CountVectorizer()
		vector.fit(x_train)
		x_train_vft = vector.transform(x_train)
		x_test_vft = vector.transform(x_test)

		count = 1
		for tweet in tweets_text:
			tweet_text = str(count)+":- "+tweet
			msg_list.insert(END,tweet_text)
			# print(count, tweet)
			count += 1
			tweet = [tweet]
			vec = vector.transform(tweet)

		# Multinomial Naive Bayes-Every feature is independent,probability is cal and highest one will be o/p,fastest
			temp = mnb.MultinomialNBAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Multinomial Naive Bayes")
			msg_list.insert(END,temp)
			
			"""
		Regression analysis is an important tool for modelling and analyzing data. Here, we fit a curve/line to the data
		Points,in such a manner that the differences b/w the distances of data points from the curve/line is minimized.
			a topic of some context.Ex:context:-road accident,topic:-car accident,it can happen or not happen
			"""

			"""
			Logistic Regression-It can give a binary or multi result(positive/negative/neutral),has a range 0 to 1
			# used for category data.Its has a curve.3 Types
			# lbgfs or lmbgfs is Limited memory Broyden–Fletcher–Goldfarb–Shanno Algo.Memory optimization algo
			# newton-cg:- newton's method for Large Bound-Constrained Optimization
			# multi-calss tells which logistic regression is being used
			"""

			# 1:-OrdinalLogisticRegression not used because it takes at lest 3 categories but we have 2,+ve and -ve

			# 2:-Multinomial Logistic Regression-Used for 2 or more category,vision-shortsight,longsight,perfect
			temp = mlr.MultinomialLRAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Multinomial Logistic Regression")
			msg_list.insert(END,temp)
			
			# 3:-Binary logistic regression-Used for 2 category,good,bad
			temp = blr.BinomialLRAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Binary logistic regression")
			msg_list.insert(END,temp)
			
		# LinearRegression-find optimal line b/w the 2 data,where one data is independent(text),and other is dependent
			# (type-pos/neg) on another
			# lr.LinearRegressionAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			"""
		SVM(support vector machine)-takes data as i/p and o/p a line that separates those classes[pos/neg] if possible
		we find the points closest to the line from both the classes.These points are called support vectors.we compute 
			the distance between the line and the support vectors. This distance is called the margin. Our goal is to 
		maximize the margin. The hyperplane for which the margin is maximum is the optimal hyperplane.Thus SVM tries to
		make a decision boundary in such a way that the separation between the two classes(that street) is as wide as 
			possible
			"""

			# Linear Classifier
			temp = lc.LinearClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Linear Classifier")
			msg_list.insert(END,temp)
			
			# LinearSupportVectorClassifier-LinearSeparationOfDataHappensOptimalLineIsDrawn using margins b/w both data
			temp = lsvc.LinearSupportVectorClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"LinearSupportVectorClassifier")
			msg_list.insert(END,temp)
			
			# Decision Tree Classifier
			temp = dtc.DecisionTreeClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Decision Tree Classifier")
			msg_list.insert(END,temp)

			# Random Forest classifier
			temp = rfc.RandomForestClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Random Forest classifier")
			msg_list.insert(END,temp)

			# Extra Trees Classifier
			temp = etc.ExtraTreesClassifierAlgo(x_train_vft, y_train, x_test_vft, y_test, vec)
			msg_list.insert(END,"Extra Trees Classifier")
			msg_list.insert(END,temp)
			msg_list.insert(END," ")

	except Exception as e:
		# Print the error
		print(e)

		# When reach the rate limit
		def on_limit(self, track):
			# Print rate limiting error
			print("Rate limited, continuing")
			# Continue mining tweets
			return True
		# When timed out

		def on_timeout(self):
			# Print timeout message
			print(sys.stderr, 'Timeout')
			# Wait 10 seconds
			time.sleep(10)
			# Return nothing
			return

if __name__ == "__main__":
	mainwindow = Tk()
	mainwindow.title("Twitter Sentimental Analysis Engine")
	Label(mainwindow, text="TWITTER SENTIMENTAL ANALYSIS ENGINE", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	photo = PhotoImage(file="Twitterlogo.png")
	Label(mainwindow, image=photo, bg="black", fg="white").pack(side=TOP, fill=X)
	
	messages_frame = Frame(mainwindow)
	scrollbar = Scrollbar(messages_frame)  # To navigate through past messages.
	# Following will contain the messages.
	msg_list = Listbox(messages_frame, height=15, width=50, yscrollcommand=scrollbar.set)
	scrollbar.pack(side=RIGHT, fill=Y,padx=2,pady=2)
	msg_list.pack(side=LEFT, fill=BOTH,padx=2,pady=2)
	msg_list.pack(padx=2,pady=2)
	messages_frame.pack()

	Label(mainwindow, text="USERNAME", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	Entry1 = Entry(mainwindow)
	Entry1.pack(side=TOP, padx=2, pady=2)
	Label(mainwindow, text="NUMBER OF TWEETS", bg="black", fg="white").pack(side=TOP, fill=X, padx=2, pady=2)
	w = Scale(mainwindow, from_=1, to=10, orient=HORIZONTAL)
	w.pack(side=TOP, fill=X, padx=2, pady=2)
	But1 = Button(mainwindow, text="RUN", command=execute)
	But1.pack(side=TOP, fill=X, padx=2, pady=2)
>>>>>>> a8eac8957e283fe23b26e99d32eac0ba302a4a04
	mainwindow.mainloop()