fixed authors error and beautified code (PEP8)

modules/headline.py

@@ -3,56 +3,53 @@
 from nltk.classify import NaiveBayesClassifier
 
 
-class title:
-	#Initialisations
-	def __init__(self): 
-		self.news_url=input("\nEnter The URL : ")
-		self.pos=[] #Variable to store all positive tokens from positive_headlines.csv file
-		self.neg=[] #Variable to store all negative tokens from negative_headlines.csv file
-
-		
+class Title:
+	# Initialisations
+	def __init__(self):
+		self.news_url = input("\nEnter The URL : ")
+		self.pos = []  # Variable to store all positive tokens from positive_headlines.csv file
+		self.neg = []  # Variable to store all negative tokens from negative_headlines.csv file
+		# self.article = newspaper.Article(news_url)
+
 	# extract headline
 	def extract_headline(self):
 		try:
 			article = newspaper.Article(self.news_url)
 			article.download()
 			article.parse()
-			return article.title.strip()
-		except newspaper.article.ArticleException: #List possible errors in case of any exception
+			return article
+		except newspaper.article.ArticleException:  # List possible errors in case of any exception
 			print("\nCONNECTION/URL ERROR: Article could not be retrieved.")
 
-
-	#Adding Training/Testing Data
-	def train(self,headline):
+	# Adding Training/Testing Data
+	def train(self, headline):
 		with open("positive_headlines.csv") as file:
 			for sentence in file:
-				self.pos.append([{word: True for word in nltk.word_tokenize(sentence)},'Positive'])
+				self.pos.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Positive'])
 
 		with open("negative_headlines.csv") as file:
 			for sentence in file:
-				self.neg.append([{word: True for word in nltk.word_tokenize(sentence)},'Negative'])
+				self.neg.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Negative'])
 
-		training=self.pos[:int(len(self.pos))] + self.neg[:int(len(self.neg))]
+		training = self.pos[:int(len(self.pos))] + self.neg[:int(len(self.neg))]
 
-		classifier = NaiveBayesClassifier.train(training) #Training
-		sentiment=classifier.classify({word: True for word in nltk.word_tokenize(headline)})
+		classifier = NaiveBayesClassifier.train(training)  # Training
+		sentiment = classifier.classify({word: True for word in nltk.word_tokenize(headline)})
 		return sentiment
 
 	# categorize headline
-	def headline_category(self,headline,sentiment):
-		print("\nHEADLINE  :",headline.upper())
-		print("SENTIMENT :",sentiment)
-		print("AUTHOR(S) :",*self.article.authors,'\n')
+	def headline_category(self, headline, sentiment):
+		print("\nHEADLINE  :", headline.upper())
+		print("SENTIMENT :", sentiment)
+		print("AUTHOR(S) :", self.extract_headline().authors, '\n')
 
-
 	# main of class
 	def main(self):
-		hdln=self.extract_headline()
-		sntmnt=self.train(hdln)
+		hdln = self.extract_headline().title.strip()
+		sntmnt = self.train(hdln)
 		self.train(hdln)
-		self.headline_category(hdln,sntmnt)
+		self.headline_category(hdln, sntmnt)
 
 
-if __name__=='__main__':
-	title().main()
-
+if __name__ == '__main__':
+	Title().main()