and write the positive and negative marked tweets
into corresponding files after the breaking the
tweets into tokens"""
<<<<<<< HEAD
m = hashlib.md5()
=======
>>>>>>> 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
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")
