def classify(line):
log_sum_pos=0.0
log_deno=0.0
# line=raw_input()
tuple=tweet_filter.sub("",word_normalize.sub(r'\1\1',sub(r'(\w)\1+\b', r'\1', line.lower())))
#Convert tweet into a list of words
feature=list(tuple.split())
for word in range(0,len(feature)-1):
bigram=filter_tweet(feature[word])+" "+filter_tweet(feature[word+1])
# Antonym replacer
bigram=neg.replace_negations(bigram)
temp=0.0
if len(bigram) > 1:
unigramList=bigram.split()
if bigram in posBigramdic:
temp=float(posBigramdic[bigram])
log_sum_pos+=log(float(temp)/(pos_dic_len/total_len))
if temp>0 :
log_deno+=log(temp)
else :
for unigram in unigramList:
if unigram in posunigramdic:
temp=float(posunigramdic[unigram])
log_sum_pos+=log(float(temp)/(pos_dic_len/total_len))
if temp>0 :
log_deno+=log(temp)
if bigram in negBigramdic:
temp+=float(negBigramdic[bigram])
if temp>0 :
log_deno+=log(temp)
else :
for unigram in unigramList:
if unigram in negunigramdic:
temp=float(negunigramdic[unigram])
if temp>0 :
log_deno+=log(temp)
else :
if bigram in posunigramdic:
temp=float(posunigramdic[bigram])
log_sum_pos+=log(float(temp)/(pos_dic_len/total_len))
if bigram in negunigramdic:
temp+=float(negunigramdic[bigram])
if temp>0 :
log_deno+=log(temp)
#print "\nTemp > 0",word,temp,log_sum_pos
#print "VALUES",log_deno,log_sum_pos
log_pos_prob=log(0.5)
#log_sum=log_sum_pos+log_sum_neg+log_pos_prob
log_sum=(log_sum_pos+log_pos_prob)-log_deno
print exp(log_sum),line
#print log_sum,line
if exp(log_sum)>limits.ClassifierThreshold:
return '1'
else:
return '0'
def classify(line):
log_sum_pos = 0.0
log_deno = 0.0
# line=raw_input()
tuple = tweet_filter.sub(
"", word_normalize.sub(r'\1\1', sub(r'(\w)\1+\b', r'\1',
line.lower())))
#Convert tweet into a list of words
feature = list(tuple.split())
for word in range(0, len(feature) - 1):
bigram = filter_tweet(feature[word]) + " " + filter_tweet(
feature[word + 1])
# Antonym replacer
bigram = neg.replace_negations(bigram)
temp = 0.0
if len(bigram) > 1:
unigramList = bigram.split()
if bigram in posBigramdic:
temp = float(posBigramdic[bigram])
log_sum_pos += log(float(temp) / (pos_dic_len / total_len))
if temp > 0:
log_deno += log(temp)
else:
for unigram in unigramList:
if unigram in posunigramdic:
temp = float(posunigramdic[unigram])
log_sum_pos += log(
float(temp) / (pos_dic_len / total_len))
if temp > 0:
log_deno += log(temp)
if bigram in negBigramdic:
temp += float(negBigramdic[bigram])
if temp > 0:
log_deno += log(temp)
else:
for unigram in unigramList:
if unigram in negunigramdic:
temp = float(negunigramdic[unigram])
if temp > 0:
log_deno += log(temp)
else:
if bigram in posunigramdic:
temp = float(posunigramdic[bigram])
log_sum_pos += log(float(temp) / (pos_dic_len / total_len))
if bigram in negunigramdic:
temp += float(negunigramdic[bigram])
if temp > 0:
log_deno += log(temp)
#print "\nTemp > 0",word,temp,log_sum_pos
#print "VALUES",log_deno,log_sum_pos
log_pos_prob = log(0.5)
#log_sum=log_sum_pos+log_sum_neg+log_pos_prob
log_sum = (log_sum_pos + log_pos_prob) - log_deno
print exp(log_sum), line
#print log_sum,line
if exp(log_sum) > limits.ClassifierThreshold:
return '1'
else:
return '0'
def classify(line,Company):
log_sum_pos=0.0
log_sum_neg=0.0
log_deno=0.0
if Company=="aapl" :
posd=aapl_posd
negd=aapl_negd
elif Company=="bac" :
posd=bac_posd
negd=bac_negd
elif Company=="goog" :
posd=goog_posd
negd=goog_negd
lne=""
tweet=tweet_filter.sub("",word_normalize.sub(r'\1\1',sub(r'(\w)\1+\b', r'\1', line.lower()))).split() #twitter hack!
for feature in tweet:
#print feature
#word=feature
if dicSpell.has_key(feature):
feature=dicSpell[feature]
#feature=sp.correct(feature)
word=stem.PorterStemmer().stem(feature)
if word not in stop:
if dicSyn.has_key(word):
word=dicSyn[word]
lne=lne+" "+word
lne = neg.replace_negations(lne)
# bi=defaultdict(int)
feature=set(list(lne.split()))
# for word in range(0,len(feature)-1):
# bi[feature[word]+" "+feature[word+1]]+=1
for word in feature:
temp=0.0
if word in posd:
temp=float(posd[word])
log_sum_pos+=log(float(temp))#/(pos_dic_len/total_len))
#print "Positive",word,log_sum_pos,posd[word],temp
if word in negd:
temp+=float(negd[word])
log_sum_neg+=log(float(temp))
#print "\nNegative",word,log_sum_pos,negd[word],temp
if temp>0 :
log_deno+=log(temp)
log_sum_pos+=log(2.1)
log_sum_neg+=log(1)
#print "\nTemp > 0",word,temp,log_sum_pos
#print "VALUES",log_deno,log_sum_pos
log_pos_prob=log(0.5)
log_neg_prob=log(0.5)
#log_sum=log_sum_pos+log_sum_neg+log_pos_prob
log_sum=(log_sum_pos+log_pos_prob)-log_deno
n_log_sum=(log_sum_neg+log_neg_prob)-log_deno
difference=exp(log_sum)-exp(n_log_sum)
print Company,difference,line
#print log_sum,line
if difference>=0.1:
return 2
else:
return 0 #BAD NEGATIVE
def classify(line, Company):
log_sum_pos = 0.0
log_sum_neg = 0.0
log_deno = 0.0
if Company == "aapl":
posd = aapl_posd
negd = aapl_negd
elif Company == "bac":
posd = bac_posd
negd = bac_negd
elif Company == "goog":
posd = goog_posd
negd = goog_negd
lne = ""
tweet = tweet_filter.sub(
"", word_normalize.sub(r'\1\1',
sub(r'(\w)\1+\b', r'\1',
line.lower()))).split() #twitter hack!
for feature in tweet:
#print feature
#word=feature
if dicSpell.has_key(feature):
feature = dicSpell[feature]
#feature=sp.correct(feature)
word = stem.PorterStemmer().stem(feature)
if word not in stop:
if dicSyn.has_key(word):
word = dicSyn[word]
lne = lne + " " + word
lne = neg.replace_negations(lne)
# bi=defaultdict(int)
feature = set(list(lne.split()))
# for word in range(0,len(feature)-1):
# bi[feature[word]+" "+feature[word+1]]+=1
for word in feature:
temp = 0.0
if word in posd:
temp = float(posd[word])
log_sum_pos += log(float(temp)) #/(pos_dic_len/total_len))
#print "Positive",word,log_sum_pos,posd[word],temp
if word in negd:
temp += float(negd[word])
log_sum_neg += log(float(temp))
#print "\nNegative",word,log_sum_pos,negd[word],temp
if temp > 0:
log_deno += log(temp)
log_sum_pos += log(2.1)
log_sum_neg += log(1)
#print "\nTemp > 0",word,temp,log_sum_pos
#print "VALUES",log_deno,log_sum_pos
log_pos_prob = log(0.5)
log_neg_prob = log(0.5)
#log_sum=log_sum_pos+log_sum_neg+log_pos_prob
log_sum = (log_sum_pos + log_pos_prob) - log_deno
n_log_sum = (log_sum_neg + log_neg_prob) - log_deno
difference = exp(log_sum) - exp(n_log_sum)
print Company, difference, line
#print log_sum,line
if difference >= 0.1:
return 2
else:
return 0 #BAD NEGATIVE
