def conclusionWeight(review, weight, goodWords, badWords, numSent):
reviewSent = review.split('.')
lastPart = reviewSent[-numSent:]
firstPart = reviewSent[:-numSent]
lastPart = ''.join(lastPart)
firstPart = ''.join(firstPart)
lastPartTokens = utils.customtokenize(lastPart)
firstPartTokens = utils.customtokenize(firstPart)
numLastGoodWords = 0;
numLastBadWords = 0
for x in lastPartTokens:
if x in goodWords:
numLastGoodWords+=1
if x in badWords:
numLastBadWords+=1
numFirstGoodWords = 0;
numFirstBadWords = 0
for x in firstPartTokens:
if x in goodWords:
numFirstGoodWords+=1
if x in badWords:
numFirstBadWords+=1
score = (numFirstGoodWords + (numLastGoodWords * weight)) - \
(numFirstBadWords + (numLastBadWords * weight))
#print("review is " + ("negative" if score < 0 else "positive"))
return score >= 0
def posminusneg(review, goodWords, badWords):
wordsInReview = utils.customtokenize(review) #69.90%
#wordsInReview = tokenizeAndRemovePOS(review, 'JJ') #64.35%
#wordsInReview = tokenizeAndRemovePOS(review, 'NN') #69.45% was ~81% after positive reviews
#wordsInReview = tokenizeAndRemovePOS(review, 'VB') #69.85%
#wordsInReview = tokenizeAndRemovePOS(review, 'RB') #68.75%
score = 0
for x in wordsInReview:
if x in goodWords:
score += 1
if x in badWords:
score -= 1
#print("review is " + ("negative" if score < 0 else "positive"))
return score >= 0
def getSuperGoodBad(topNum, posTuples, negTuples, dataSetGoodWords, dataSetBadWords):
# 80% will be train set and 20% test set
percenttrain = 0.8
byWeight = True # toggles equal weighting of words versus weighting by frequency
printing = False # turn this on if you want to see more detailed info
posLen = len(posTuples)
trainCount = int(percenttrain*posLen)
testCount = posLen - trainCount
random.shuffle(posTuples)
random.shuffle(negTuples)
trainPosTuples = posTuples[:trainCount]
trainNegTuples = negTuples[:trainCount]
testPosReviews = [tup[0] for tup in posTuples[trainCount:]]
testNegReviews = [tup[0] for tup in negTuples[trainCount:]]
# probly could optimize this part
superPospos = []
for tup in trainPosTuples:
superPospos += list(set(tup[1]))
superNegpos = []
for tup in trainNegTuples:
superNegpos += list(set(tup[1]))
goodFreqDist = FreqDist(superPospos);
badFreqDist = FreqDist(superNegpos);
topGoodWords = []
topBadWords = []
topGoodDict = {}
topBadDict = {}
# build custom top good and bad word lists
while len(topGoodWords) < topNum or len(topBadWords) < topNum:
if len(topGoodWords) < topNum:
goodTup = goodFreqDist.most_common(1)[0]
goodWord = goodTup[0]
goodWeight = goodTup[1]
topGoodDict[goodWord] = goodWeight
del goodFreqDist[goodWord]
if goodWord in dataSetGoodWords:
if goodWord in topBadWords:
topBadWords.remove(goodWord)
else:
topGoodWords.append(goodWord)
if len(topBadWords) < topNum:
badTup = badFreqDist.most_common(1)[0]
badWord = badTup[0];
badWeight = badTup[1]
topBadDict[badWord] = badWeight
del badFreqDist[badWord]
if badWord in dataSetBadWords:
if badWord in topGoodWords:
topGoodWords.remove(badWord)
else:
topBadWords.append(badWord)
topGoodCheck = [(word,topGoodDict[word]) for word in topGoodWords]
topBadCheck = [(word,topBadDict[word]) for word in topBadWords]
topGoodDict = {}
topBadDict = {}
# swap weights of each set to equalize versus appearance frequency
for i in range(len(topGoodCheck)):
gtup = topGoodCheck[i]
btup = topBadCheck[i]
topGoodDict[gtup[0]] = btup[1]
topBadDict[btup[0]] = gtup[1]
# alternate strategy
#topGoodDict[gtup[0]] = trainCount - gtup[1]
#topBadDict[btup[0]] = trainCount - btup[1]
count = 0
correct = 0
avgposscore = 0
avgnegscore = 0
curacc = 0
for posReview in testPosReviews:
count+=1
score = 0
reviewTokens = utils.customtokenize(posReview.lower())
for token in reviewTokens:
if token in topGoodWords:
score += topGoodDict[token] if byWeight else 1
if token in topBadWords:
score -= topBadDict[token] if byWeight else 1
avgposscore += score
if score > 0:
if printing: print("correct! " + str(score))
correct+=1
else:
if printing: print("wrong " + str(score))
curacc = correct/count
for negReview in testNegReviews:
count+=1
score = 0
reviewTokens = utils.customtokenize(negReview.lower())
for token in reviewTokens:
if token in topGoodWords:
score += topGoodDict[token] if byWeight else 1
if token in topBadWords:
score -= topBadDict[token] if byWeight else 1
avgnegscore += score
if score <= 0:
if printing: print("correct! " + str(score))
correct+=1
else:
if printing: print("wrong " + str(score))
finalacc = correct / count
if printing:
print("avg positive score : " + "{:.4f}".format(avgposscore / testCount))
print("avg negative score : " + "{:.4f}".format(avgnegscore / testCount))
print("positive accuracy : " + "{:.4f}".format(curacc))
#calculate positive accuracy from final and negative
curacc += (finalacc - curacc)*2
print("negative accuracy : " + "{:.4f}".format(curacc))
print("final accuracy : " + "{:.4f}".format(finalacc))
return finalacc
