Python make_token Beispiele

Beispiel #1

Datei: trainPhase1.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

def testModel():
  acc=0
  for t in test:
    tokens = we.make_token(t[1:])
    prob = knn.knnclassifier(knnpos,knnneg,tokens.keys(),5)
    if((prob[1]>prob[0])==(t[0]=="1") or (prob[0]>prob[1])==(t[0]=="0")):
      acc = acc+1
  return (acc/462.0)*100

Beispiel #2

Datei: trainPhase1.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

def addNegKeywords(index):
  tokens = we.make_token(label[index][1:])
  l = Set()
  for t in tokens:
    if t in negkeywords:
      l.add(t)
  if(len(l)!=0):
    knnneg.append(l)
  return l

Beispiel #3

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

def addNegKeywords(ln):
  tokens = we.make_token(ln)
  l = Set()
  for t in tokens:
    #if t in negkeywords :
      neg.add(t)
      l.add(t)
  if(len(l)!=0):
    knnneg.append(l)
  return l

Beispiel #4

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

def addPosKeywords(lp):
  tokens = we.make_token(lp)
  l = Set()
  for t in tokens:
    #if t in poskeywords :
      pos.add(t)
      l.add(t)
  if(len(l)!=0):
    knnpos.append(l)
  return l

Beispiel #5

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

def addNegKeywords(ln):
    tokens = we.make_token(ln)
    l = Set()
    for t in tokens:
        #if t in negkeywords :
        neg.add(t)
        l.add(t)
    if (len(l) != 0):
        knnneg.append(l)
    return l

Beispiel #6

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

def addPosKeywords(lp):
    tokens = we.make_token(lp)
    l = Set()
    for t in tokens:
        #if t in poskeywords :
        pos.add(t)
        l.add(t)
    if (len(l) != 0):
        knnpos.append(l)
    return l

Beispiel #7

Datei: trainPhase2.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

def testModel():
  acc=0
  for t in test:
    tokens = we.make_token(t[1:])
    testlabel = knn.knnclassifier2(knnpos,knnneg,tokens.keys(),5)
    if((testlabel==1)==(t[0]=="1") or (testlabel==0)==(t[0]=="0")):
      acc = acc+1
#    else:
 #     print t
  #    print we.tagged_tokens(t[1:])"""
  return (acc/3662.0)*100

Beispiel #8

Datei: trainPhase2.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

def addNegKeywords(index):
  global negl
  tokens = we.make_token(label[index][1:])
  l = []
  for t in tokens:
    if t in negkeywords:
      if(neg.has_key(t)):
        neg[t]+=1
      else:
        neg[t]=1
      negl+=1
      l.append(t)
  if(len(l)!=0):
    knnneg.append(l)
  return l

Beispiel #9

Datei: trainPhase2.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

def addPosKeywords(index):
  global posl
  tokens = we.make_token(label[index][1:])
  l = []
  for t in tokens:
    if t in poskeywords:
      if(pos.has_key(t)):
        pos[t]+=1
      else:
        pos[t]=1
      posl+=1
      l.append(t)
  if(len(l)!=0):
    knnpos.append(l)
  return l

Beispiel #10

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

  for t in tokens:
    #if t in negkeywords :
      neg.add(t)
      l.add(t)
  if(len(l)!=0):
    knnneg.append(l)
  return l

train = []

index = 0
while(maxp>0):
  if label[index][0] == "1":
    train.append(label[index]) 
    maxp -=1
    tokens = we.make_token(label[index][1:])
    l = Set()
    for t in tokens:
      if t in poskeywords :
        pos.add(t)
        l.add(t)
    if(len(l)!=0):
      knnpos.append(l)
  index+=2

index = 1
while(maxn>0):
  if label[index][0] == "0":
    train.append(label[index]) 
    maxn -=1
    tokens = we.make_token(label[index][1:])

Beispiel #11

Datei: trainPhase1.py Projekt: lokeshj1703/Semantic-Analysis-Using-Active-Learning

#print test
def testModel():
  acc=0
  for t in test:
    tokens = we.make_token(t[1:])
    prob = knn.knnclassifier(knnpos,knnneg,tokens.keys(),5)
    if((prob[1]>prob[0])==(t[0]=="1") or (prob[0]>prob[1])==(t[0]=="0")):
      acc = acc+1
  return (acc/462.0)*100

index = 0
while(maxp>0):
  if label[index][0] == "1":
    maxp -=1
    tokens = we.make_token(label[index][1:])
    l = Set()
    for t in tokens:
      if t in poskeywords :
        pos.add(t)
        l.add(t)
    if(len(l)!=0):
      knnpos.append(l)
  index+=2

index = 1
while(maxn>0):
  if label[index][0] == "0":
    maxn -=1
    tokens = we.make_token(label[index][1:])
    l = Set()

Beispiel #12

Datei: run.py Projekt: samishbedi/Active-Learning-Classification-of-Sentiment-Polarity-of-Movie-reviews

        #if t in negkeywords :
        neg.add(t)
        l.add(t)
    if (len(l) != 0):
        knnneg.append(l)
    return l


train = []

index = 0
while (maxp > 0):
    if label[index][0] == "1":
        train.append(label[index])
        maxp -= 1
        tokens = we.make_token(label[index][1:])
        l = Set()
        for t in tokens:
            if t in poskeywords:
                pos.add(t)
                l.add(t)
        if (len(l) != 0):
            knnpos.append(l)
    index += 2

index = 1
while (maxn > 0):
    if label[index][0] == "0":
        train.append(label[index])
        maxn -= 1
        tokens = we.make_token(label[index][1:])