def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
pdata = parser.parseFiles(fileList)
allsent = ''
for f in pdata:
allsent += f[3]
all_words = FreqDist(w.lower()
for w in word_tokenize(allsent)
if w not in stopwords.words('english') )
global top_words
top_words = all_words.keys()[:500]
# pdata = getParseData()
featdata = featureAggregator(pdata)
print featdata[:10]
def main():
classifier = getClassifier()
userInput = getInput()
fileList = parser.getFiles(userInput['test'])
pdata = parser.parseFiles(fileList)
featdata = extractor.featureAggregator(pdata)
print pdata[4]
output = []
outputFileObj = open('../../output.txt', 'w')
# if pdata[4]
for featdatarow in featdata:
cl = classifier.classify(featdatarow[4])
if cl == 'pos':
label = '1'
elif cl == 'neutral':
label = '0'
else:
label = '-1'
print featdatarow[0], featdatarow[1], label
outputRow = str(featdatarow[0]) + '\t' + str(featdatarow[1]) + '\t' + str(label) + '\n'
output.append(outputRow)
outputFileObj.write(outputRow)
outputFileObj.close()
def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
parsedata = parser.parseFiles(fileList)
allsent = ''
for f in parsedata:
allsent += f[3]
all_words = FreqDist(w.lower()
for w in word_tokenize(allsent)
if w not in stopwords.words('english') )
global top_words
top_words = all_words.keys()[:500]
featdata = extractor.featureAggregator(parsedata)
# print featdata[20]
print "Sample Data Item:\n\n"
print "%20s %4s %4s %20s" % ("FILENAME", "LINENUM", "VOTE", "SENTENCE" )
print "-" * 79
print "%10s %4s %4s %20s" % (featdata[20][0], featdata[20][1], featdata[20][2], featdata[20][3])
print "\n\nFeatures of this Data Item"
print "-" * 79
for key,val in featdata[20][4].items():
print "%50s : %10s" % (key, val )
# print "A sample feature: %s" % (featdata[20][4])
allacc = splitfeatdata(featdata)
print "\n\n"
print "-" * 60
print "Accuracy Values: %s" % (allacc)
print "==" * 60
print "Overall Classifier Accuracy %4.4f " % (sum(allacc)/len(allacc))
def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
pdata = parser.parseFiles(fileList)
allsent = ''
for f in pdata:
allsent += f[3]
all_words = FreqDist(w.lower() for w in word_tokenize(allsent)
if w not in stopwords.words('english'))
global top_words
top_words = all_words.keys()[:500]
# pdata = getParseData()
featdata = featureAggregator(pdata)
print featdata[:10]
