Implemented NLTK

Idea/1. Headline.md

@@ -1,4 +1,5 @@
-# 1. Headline
+# 1. Headline 
+~Not Up to date~
 
 ### Ideas for executing a headline rating
 

ReadMe.md

@@ -18,4 +18,7 @@ The program aims to eliminate the prevailing dominance of Fake News all around t
 
 * AlphaPhiKappa
 * riseandshine0
+* mphirke
+* mandjevant
 * webdotorg
+

modules/headline.py

@@ -1,55 +1,63 @@
-import urllib.request
-from bs4 import BeautifulSoup
-from textblob.classifiers import NaiveBayesClassifier
-from textblob import TextBlob
+import newspaper
+import nltk
+from nltk.classify import NaiveBayesClassifier
+from nltk.classify.util import accuracy
 
 class title:
-
     #Initialisations
+
+
     def __init__(self): 
-        self.news_url="https://edition.cnn.com/2019/08/25/politics/trump-g7-boris-johnson-emmanuel-macron/index.html"
+        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
 
 
     def extract_headline(self):
-        self.net_con=True #Expecting Internet Connection to be working initially
+
         try:
-            news_page=urllib.request.urlopen(self.news_url)   
-            soup = BeautifulSoup(news_page,'html.parser')
-            headline_in_html=soup.find('h1')
-            headline=headline_in_html.text.strip()
-            return headline
+            self.article = newspaper.Article(self.news_url)
+            self.article.download()
+            self.article.parse()
+
+        except newspaper.article.ArticleException: #List possible errors in case of any exception
+            print("\nCONNECTION/URL ERROR: There may be a problem with your connection or the URL entered may be invalid")
+            article.title = "Invalid URL/Could not extract title"
 
-        except urllib.error.URLError:
-            print("\nCONNECTIION ERROR:There may be a connection problem. Please check if the device is connected to the Internet")
-            self.net_con=False #Value update if the program is unable to connenct
+        return self.article.title.strip()
 
 
-    #Adding Training Data
-    def train_data(self, headline):
-        try:
-            with open('training_data.csv','r') as td:
-                cl=NaiveBayesClassifier(td,format='csv')
-                sentiment=cl.classify(headline)
-                return sentiment
+    #Adding Training/Testing Data
+    def train(self,headline):
 
-        except:
-            if self.net_con==False:
-                pass
-            else:
-                print("\n\nProgram Error")
+        with open("positive_headlines.csv") as file:
+            for sentence in file:
+                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'])
+
+        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)})
+        return sentiment
 
 
     def headline_category(self,headline,sentiment):
+        print("\nHEADLINE  :",headline.upper())
+        print("SENTIMENT :",sentiment)
+        print("AUTHOR(S) :",*self.article.authors,'\n')
 
-        analyse_headline=TextBlob(headline)
-        print("\n"+"Headline:",headline,"\n")
-        print("Headline Sentiment:",sentiment,"\n\n")
 
     def main(self):
         hdln=self.extract_headline()
-        sntmnt=self.train_data(hdln)
+        sntmnt=self.train(hdln)
+        self.train(hdln)
         self.headline_category(hdln,sntmnt)
-
+
+
 if __name__=='__main__':
     do_ya_thing=title()
-    do_ya_thing.main()
+    do_ya_thing.main()

modules/negative_headlines.csv

@@ -0,0 +1,42 @@
+aba decides against community broadcasting licence
+act fire witnesses must be aware of defamation
+air nz staff in aust strike for pay rise
+air nz strike to affect australian travellers
+aussie qualifier stosur wastes four memphis match
+australia is locked into war timetable opp
+blizzard buries united states in bills
+brigadier dismisses reports troops harassed in
+british combat troops arriving daily in kuwait
+bryant leads lakers to double overtime win
+bushfire victims urged to see centrelink
+businesses should prepare for terrorist attacks
+carews freak goal leaves roma in ruins
+cemeteries miss out on funds
+council chief executive fails to secure  position
+crean tells alp leadership critics to shut up
+dargo fire threat expected to rise
+death toll continues to climb in south korean subway
+direct anger at govt not soldiers crean urges
+dispute over at smithton vegetable processing plant
+dying korean subway passengers phoned for help
+firefighters contain acid spill
+four injured in head on highway crash
+gilchrist backs rest policy
+girl injured in head on highway crash
+govt is to blame for ethanols unpopularity opp
+griffiths under fire over project knock back
+hacker gains access to eight million credit cards
+hanson should go back where she came from nsw mp
+harrington raring to go after break
+investigation underway into elster creek spill
+iraqs neighbours plead for continued un inspections
+israeli forces push into gaza strip
+kelly not surprised ethanol confidence low
+korean subway fire 314 still missing
+low demand forces air service cuts
+man with knife hijacks light plane
+more than 40 pc of young men drink alcohol at
+more water restrictions predicted for northern tas
+petrol bombs and water cannons mark violent escalation in hong kong protests
+imran khan addresses pakistan on kashmir threatens nuclear war once again
+FIR against NCP leader Ajit Pawar 69 others in Maharashtra co-op bank scam case

modules/positive_headlines.csv

@@ -0,0 +1,45 @@
+ag calls for infrastructure protection summit
+ambitious olsson wins triple jump
+antic delighted with record breaking barca
+aust addresses un security council over iraq
+australia to contribute 10 million in aid to iraq
+barca take record as robson celebrates birthday in
+bathhouse plans move ahead
+big hopes for launceston cycling championship
+big plan to boost paroo water supplies
+commonwealth bank cuts fixed home loan rates
+community urged to help homeless youth
+councillor to contest wollongong as independent
+council moves to protect tas heritage garden
+council welcomes ambulance levy decision
+council welcomes insurance breakthrough
+dems hold plebiscite over iraqi conflict
+epa still trying to recover chemical clean up costs
+freedom records net profit for third successive
+funds allocated for domestic violence victims
+funds allocated for youth at risk
+funds announced for bridge work
+funds to go to cadell upgrade
+funds to help restore cossack
+golf club feeling smoking ban impact
+greens offer police station alternative
+hanson is grossly naive over nsw issues costa
+health minister backs organ and tissue storage
+heavy metal de posits survey nearing end
+investigations underway into death toll of korean
+iraq to pay for own rebuilding white house
+irish man arrested over omagh bombing
+irrigators vote over river management
+jury to consider verdict in murder case
+juvenile sex offenders unlikely to reoffend as
+last minute call hands alinghi big lead
+man arrested after central qld hijack attempt
+man charged over cooma murder
+man fined after aboriginal tent embassy raid
+man jailed over keno fraud
+massive drug crop discovered in western nsw
+mayor warns landfill protesters
+meeting to consider tick clearance costs
+meeting to focus on broken hill water woes
+moderate lift in wages growth
+Chandrayaan-2 maps lunar surface takes striking photos of craters on Moon

test/test.py

@@ -0,0 +1,17 @@
+def train_classifier(self,headline):
+
+    a=input("""\nIf you think the output was incorrect, Please re-label the headline's sentiment to train the classifier 
+     & help improve future predictions [p/n]: """)
+
+    if a=='p':
+        with open('positive_headlines.csv','a') as td:
+                td.write('\n'+headline)
+    elif a=='n':
+        with open('negative_headlines.csv','a') as td:
+                td.write('\n'+headline)
+    else:
+        print("Incorrect key pressed!")
+    pass
+
+
+self.train_classifier(hdln)