def classify_tweets(request):
consumer_key="Wb4W1n264iHhcrqcXt54bA"
consumer_secret="2NFs7pO610XKQUOs5hPAz8wCEO4uxmP3111HPhsmgc"
access_token="36641014-28RR3YAp6MxFxJ706gsp5a7bRy0sYDsjLCwixs2iM"
access_token_secret="qOGQg84VvurJKX9qSF3Zgl973BxF6ryt7Yruoxtw"
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
query = request.POST.get('query')
result=api.search(query)
tweets=[]
classification=[]
for tweet in result:
try:
tweets.append(str(tweet.text))
except:
pass
posScore=0
negScore=0
for tweet in tweets:
tokens=tweet.split()
data_preprocess.remove_noise_words(tokens)
data_preprocess.remove_names(tokens)
data_preprocess.remove_links(tokens)
tweet_counts=[]
token_counts=[]
category_counts=defaultdict(lambda:defaultdict(int))
p=tweet_category_count.objects.get(id=1)
tweet_counts.append(p.positive_count)
tweet_counts.append(p.negative_count)
p=token_category_count.objects.get(id=1)
token_counts.append(p.positive_count)
token_counts.append(p.negative_count)
for token in tokens:
try:
p=pos_tokens.objects.get(ptoken=token)
category_counts[token]['pos']=p.pcount
except:
category_counts[token]['pos']=0
for token in tokens:
try:
p=neg_tokens.objects.get(ntoken=token)
category_counts[token]['neg']=p.ncount
except:
category_counts[token]['neg']=0
classifier=NaiveBayesClassifier()
result=classifier.classify(tokens,category_counts,tweet_counts,token_counts)
if(result=='pos'):
posScore+=1
else:
negScore+=1
classification.append(result)
return render_to_response("index.html",{'tweets':tweets,'pos_neg':classification,'posScore':posScore,'negScore':negScore})
=======
>>>>>>> 89aa7e23b0b2789d0986b7c01aff694715d5590b
for index in request.POST:
(ind,a)=index.split(':')
if request.POST.get(index,''):
if(request.POST[index]=='pos'):
out_file=open("positive.txt",'a')
<<<<<<< HEAD
m.update(current_tweets[int(a)-1])
p=data_set(tweet=current_tweets[int(a)-1],tweet_hash=m.hexdigest(),pos_neg='pos',movie_name=current_tweets[0])
p.save()
else:
m.update(current_tweets[int(a)-1])
out_file=open("negative.txt",'a')
p=data_set(tweet=current_tweets[int(a)-1],tweet_hash=m.hexdigest(),pos_neg='neg',movie_name=current_tweets[0])
p.save()
tokens=current_tweets[int(a)-1].split()
data_preprocess.remove_noise_words(tokens)
data_preprocess.remove_names(tokens)
data_preprocess.remove_links(tokens)
=======
else:
out_file=open("negative.txt",'a')
tokens=current_tweets[int(a)-1].split()
>>>>>>> 89aa7e23b0b2789d0986b7c01aff694715d5590b
for token in tokens:
out_file.write(token+'\n')
return render_to_response("tweetsSaved.html")
def save_tweets(request):
#unpickle the list of recent tweets retrieved
with open('current_tweets.txt','rb') as file_id:
current_tweets=pickle.load(file_id)
tokens=[]
"""Examine every Post variable one by one
and write the positive and negative marked tweets
into corresponding files after breaking the
tweets into tokens"""
m = hashlib.md5()
for index in request.POST:
(ind,a)=index.split(':')
if request.POST.get(index,''):
if(request.POST[index]=='pos'):
out_file=open("positive.txt",'a')
m.update(current_tweets[int(a)-1])
p=data_set(tweet=current_tweets[int(a)-1],tweet_hash=m.hexdigest(),pos_neg='pos',movie_name=current_tweets[0])
p.save()
#Update the number of positive tweets seen so far
try:
p=tweet_category_count.objects.get(id=1)
p.positive_count=p.positive_count+1
p.save()
except:
p=tweet_category_count(id=1,positive_count=1,negative_count=0)
p.save()
else:
out_file=open("negative.txt",'a')
m.update(current_tweets[int(a)-1])
p=data_set(tweet=current_tweets[int(a)-1],tweet_hash=m.hexdigest(),pos_neg='neg',movie_name=current_tweets[0])
p.save()
#Update the number of negative tweets seen so far
try:
p=tweet_category_count.objects.get(id=1)
p.negative_count=p.negative_count+1
p.save()
except:
p=tweet_category_count(id=1,positive_count=0,negative_count=1)
p.save()
tokens=current_tweets[int(a)-1].split()
#Removing noise words,names (@) and hyperlinks from the tweets
data_preprocess.remove_noise_words(tokens)
data_preprocess.remove_names(tokens)
data_preprocess.remove_links(tokens)
#Removing the name of movie from the token list
movieName = current_tweets[0].split()
tokens = set(tokens)-set(movieName)
for token in tokens:
out_file.write(token+'\n')
if(request.POST[index]=='pos'):
try:
q=token_category_count.objects.get(id=1)
q.positive_count=q.positive_count+1
q.save()
except:
p=token_category_count(id=1,positive_count=1,negative_count=0)
p.save()
try:
q=pos_tokens.objects.get(ptoken=token)
#Adding 1 to the count of positive tokens
q.pcount = q.pcount + 1
q.save()
except:
r = pos_tokens(ptoken=token,pcount=1)
r.save()
else:
try:
q=token_category_count.objects.get(id=1)
q.negative_count=q.negative_count+1
q.save()
except:
p=token_category_count(id=1,positive_count=0,negative_count=1)
p.save()
try:
q=neg_tokens.objects.get(ntoken=token)
#Adding 1 to the count of negative tokens
q.ncount = q.ncount + 1
q.save()
except:
r = neg_tokens(ntoken=token,ncount=1)
r.save()
return render_to_response("tweetsSaved.html")