def __init__(self,traing_data_fileP1='mood_traing_p1.dat',traing_data_fileP2='mood_traing.dat',data_file='tweets_raw.dat'):
if self.sentiwordnet:
print "using sentiwordnet dictionary"
else:
print "not using sentiwordnet dictionary"
self.clsP1 = MoodDetectTrainer(data_file = traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file = traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(os.curdir, os.path.normpath('../data/' + data_file)) ,'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(os.curdir , os.path.normpath('../data/' + data_file)) ,'rb')
self.limit['en'] = 300000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/' + conf.SENTIWORDNET_DICT_FILENAME
self.swn = SentiWordNetCorpusReader(swn_filename)
def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
if self.sentiwordnet:
print "using sentiwordnet dictionary"
else:
print "not using sentiwordnet dictionary"
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.limit['en'] = 300000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/' + conf.SENTIWORDNET_DICT_FILENAME
self.swn = SentiWordNetCorpusReader(swn_filename)
def __init__(self,traing_data_fileP1='mood_traing_p1.dat',traing_data_fileP2='mood_traing.dat',data_file='tweets_raw.dat'):
self.clsP1 = MoodDetectTrainer(data_file = traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file = traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(self.dataDir,data_file),'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(self.dataDir,data_file),'rb')
self.limit['en'] = 150000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/SentiWordNet_3.0.0_20100705.txt'
self.swn = SentiWordNetCorpusReader(swn_filename)
def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.limit['en'] = 150000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/SentiWordNet_3.0.0_20100705.txt'
self.swn = SentiWordNetCorpusReader(swn_filename)
class RawClassifier(object):
statsData = {}
limit = {}
skip = 0
p2_f_limit = 0.6
sentiwordnet = conf.USE_SENTIWORDNET_DICT
def __init__(self,traing_data_fileP1='mood_traing_p1.dat',traing_data_fileP2='mood_traing.dat',data_file='tweets_raw.dat'):
if self.sentiwordnet:
print "using sentiwordnet dictionary"
else:
print "not using sentiwordnet dictionary"
self.clsP1 = MoodDetectTrainer(data_file = traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file = traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(os.curdir, os.path.normpath('../data/' + data_file)) ,'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(os.curdir , os.path.normpath('../data/' + data_file)) ,'rb')
self.limit['en'] = 300000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/' + conf.SENTIWORDNET_DICT_FILENAME
self.swn = SentiWordNetCorpusReader(swn_filename)
def classifyP1(self,stripSmiles=False):
self.classifiyRaw(self.tweetsFile,stripSmiles)
self.clsP1.train(self.training_data_p1)
print "done training P1"
print self.statsData
def classifyP2(self):
"""
remove noisy n-grams
"""
_st={'tf':0,'df':0}
for feutures,label in self.training_data_p1:
lang = feutures.pop('x_lang')
feuturesP2 = feutures.copy()
for f,v in feutures.items():
prob = self.clsP1.classifier.prob_classify({f:v,'x_lang':lang})
_st['tf']+=1
if max(prob.prob('n'),prob.prob('p')) <= self.p2_f_limit:
del feuturesP2[f]
_st['df']+=1
if len(feuturesP2) >= 3:
feuturesP2['x_lang']=lang
self.training_data_p2.append((feuturesP2,label))
else:
pass
print 'p2_length:' , len(self.training_data_p2), ' p1_lenght:' , len(self.training_data_p1)
print 'st:' , _st
print "deleting p1 set"
del self.training_data_p1
del self.clsP1
print "Done deleting p1 set"
self.clsP2.train(self.training_data_p2)
def stripSmiles(self,text):
emos = [':)',':-)',';-)',': )',':d','=)',':p',';)','<3',':(',':-(',': (']
for item in emos:
text = text.replace(item,"")
return text
def stats(self,lang,mood):
if not self.statsData.has_key(lang):
self.statsData[lang] = {'n':0,'p':0}
if self.limit.has_key(lang):
limit = self.limit[lang]
else:
limit = self.limit['default']
if self.statsData[lang][mood] >= limit:
return 0
else:
self.statsData[lang][mood]+=1
return 1
# CHECK WITH SENTIWORDNET
def checkWithSentiwordnet(self, text):
count = 0
tokens = nltk.word_tokenize(text)
#TODO more languages
#tokens = [w for w in tokens if not w in nltk.corpus.stopwords.words('english')]
if len(tokens) > 0:
for token in tokens:
synsets = self.swn.senti_synsets(token)
if len(synsets) > 0:
# TODO no tiene por que ser este lemma. Comprobar la categoria
lemma = synsets[0]
count = count + lemma.pos_score - lemma.neg_score
#print count, " points for tokens :", tokens
if count > 0.5:
return 'p'
if count < 0.5:
return 'n'
return 'x'
# CHECK WITH FINANCIAL DICTIONARIES
def checkWithFinancialDict(self,text):
count = self.containsPositiveWord(text) + self.containsNegativeWord(text);
if count > 0:
return 'p'
if count < 0:
return 'n'
return 'x'
def containsPositiveWord(self,text):
count = 0
for item in dictionary.positive:
if item in text:
count += 1
#print 'p:',item
return count
def containsNegativeWord(self,text):
count = 0
for item in dictionary.negative:
if item in text:
#print 'n:', item
count -= 1
return count
def classifiyRaw(self,file,stripSmiles):
while True:
try:
tweet = cPickle.load(file)
except EOFError:
print "done classify"
break
except:
print "error"
pass
if self.skip > 0:
print "skip"
self.skip -= 1
continue
if tweet:
text = unicode(tweet.get('text'))
if text.lower().find('rt ') != -1:
print 'rt'
continue
lang = self.langClassifier.detect(text)
# TODO more languages
if lang[0] != 'en':
continue
if stripSmiles:
text = self.stripSmiles(text)
if self.sentiwordnet:
mood = self.checkWithSentiwordnet(text)
else:
mood = self.checkWithFinancialDict(text)
if mood == 'x':
continue
sres = self.stats(lang[0], mood)
if sres == 0:
# limite de idioma alcanzado
print 'limit reached for ' , lang[0]
continue
if sres == -1:
print "done for %s" % mood
break
if self.count and self.count % 100 == 0:
print "classified %d tweets" % (self.count)
self.count += 1
self.training_data_p1.addRow(text, mood, lang[0])
def countRows(self,file):
rows = 0
breakes = 0
while True:
try:
tweet = cPickle.load(file)
rows +=1
except EOFError:
break
except:
breakes +=1
print 'tweets:',rows,' breakes:',breakes
class RawClassifier(object):
statsData = {}
limit = {}
skip = 0
p2_f_limit = 0.6
sentiwordnet = conf.USE_SENTIWORDNET_DICT
def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
if self.sentiwordnet:
print "using sentiwordnet dictionary"
else:
print "not using sentiwordnet dictionary"
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.limit['en'] = 300000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/' + conf.SENTIWORDNET_DICT_FILENAME
self.swn = SentiWordNetCorpusReader(swn_filename)
def classifyP1(self, stripSmiles=False):
self.classifiyRaw(self.tweetsFile, stripSmiles)
self.clsP1.train(self.training_data_p1)
print "done training P1"
print self.statsData
def classifyP2(self):
"""
remove noisy n-grams
"""
_st = {'tf': 0, 'df': 0}
for feutures, label in self.training_data_p1:
lang = feutures.pop('x_lang')
feuturesP2 = feutures.copy()
for f, v in feutures.items():
prob = self.clsP1.classifier.prob_classify({
f: v,
'x_lang': lang
})
_st['tf'] += 1
if max(prob.prob('n'), prob.prob('p')) <= self.p2_f_limit:
del feuturesP2[f]
_st['df'] += 1
if len(feuturesP2) >= 3:
feuturesP2['x_lang'] = lang
self.training_data_p2.append((feuturesP2, label))
else:
pass
print 'p2_length:', len(self.training_data_p2), ' p1_lenght:', len(
self.training_data_p1)
print 'st:', _st
print "deleting p1 set"
del self.training_data_p1
del self.clsP1
print "Done deleting p1 set"
self.clsP2.train(self.training_data_p2)
def stripSmiles(self, text):
emos = [
':)', ':-)', ';-)', ': )', ':d', '=)', ':p', ';)', '<3', ':(',
':-(', ': ('
]
for item in emos:
text = text.replace(item, "")
return text
def stats(self, lang, mood):
if not self.statsData.has_key(lang):
self.statsData[lang] = {'n': 0, 'p': 0}
if self.limit.has_key(lang):
limit = self.limit[lang]
else:
limit = self.limit['default']
if self.statsData[lang][mood] >= limit:
return 0
else:
self.statsData[lang][mood] += 1
return 1
# CHECK WITH SENTIWORDNET
def checkWithSentiwordnet(self, text):
count = 0
tokens = nltk.word_tokenize(text)
#TODO more languages
#tokens = [w for w in tokens if not w in nltk.corpus.stopwords.words('english')]
if len(tokens) > 0:
for token in tokens:
synsets = self.swn.senti_synsets(token)
if len(synsets) > 0:
# TODO no tiene por que ser este lemma. Comprobar la categoria
lemma = synsets[0]
count = count + lemma.pos_score - lemma.neg_score
#print count, " points for tokens :", tokens
if count > 0.5:
return 'p'
if count < 0.5:
return 'n'
return 'x'
# CHECK WITH FINANCIAL DICTIONARIES
def checkWithFinancialDict(self, text):
count = self.containsPositiveWord(text) + self.containsNegativeWord(
text)
if count > 0:
return 'p'
if count < 0:
return 'n'
return 'x'
def containsPositiveWord(self, text):
count = 0
for item in dictionary.positive:
if item in text:
count += 1
#print 'p:',item
return count
def containsNegativeWord(self, text):
count = 0
for item in dictionary.negative:
if item in text:
#print 'n:', item
count -= 1
return count
def classifiyRaw(self, file, stripSmiles):
while True:
try:
tweet = cPickle.load(file)
except EOFError:
print "done classify"
break
except:
print "error"
pass
if self.skip > 0:
print "skip"
self.skip -= 1
continue
if tweet:
text = unicode(tweet.text)
if text.lower().find('rt ') != -1:
print 'rt'
continue
lang = self.langClassifier.detect(text)
# TODO more languages
if lang[0] != 'en':
continue
if stripSmiles:
text = self.stripSmiles(text)
if self.sentiwordnet:
mood = self.checkWithSentiwordnet(text)
else:
mood = self.checkWithFinancialDict(text)
if mood == 'x':
continue
sres = self.stats(lang[0], mood)
if sres == 0:
# limite de idioma alcanzado
print 'limit reached for ', lang[0]
continue
if sres == -1:
print "done for %s" % mood
break
if self.count and self.count % 100 == 0:
print "classified %d tweets" % (self.count)
self.count += 1
self.training_data_p1.addRow(text, mood, lang[0])
def countRows(self, file):
rows = 0
breakes = 0
while True:
try:
tweet = cPickle.load(file)
rows += 1
except EOFError:
break
except:
breakes += 1
print 'tweets:', rows, ' breakes:', breakes
class RawClassifier(object):
statsData = {}
dataDir = "/home/toni/git/financial-twitter-sentiment-analyzer/tracker/data"
limit = {}
skip = 0
p2_f_limit = 0.75
def __init__(self,traing_data_fileP1='mood_traing_p1.dat',traing_data_fileP2='mood_traing.dat',data_file='tweets_raw.dat'):
self.clsP1 = MoodDetectTrainer(data_file = traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file = traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(self.dataDir,data_file),'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(self.dataDir,data_file),'rb')
self.limit['en'] = 150000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/SentiWordNet_3.0.0_20100705.txt'
self.swn = SentiWordNetCorpusReader(swn_filename)
def classifyP1(self,stripSmiles=False):
self.classifiyRaw(self.tweetsFile,stripSmiles)
self.clsP1.train(self.training_data_p1)
print "done training P1"
print self.statsData
def classifyP2(self):
"""
remove noisy n-grams
"""
_st={'tf':0,'df':0}
for feutures,label in self.training_data_p1:
lang = feutures.pop('x_lang')
feuturesP2 = feutures.copy()
for f,v in feutures.items():
prob = self.clsP1.classifier.prob_classify({f:v,'x_lang':lang})
_st['tf']+=1
if max(prob.prob('n'),prob.prob('p')) <= self.p2_f_limit:
del feuturesP2[f]
_st['df']+=1
if len(feuturesP2) >= 3:
feuturesP2['x_lang']=lang
self.training_data_p2.append((feuturesP2,label))
else:
pass
print 'p2_length:' , len(self.training_data_p2), ' p1_lenght:' , len(self.training_data_p1)
print 'st:' , _st
print "deleting p1 set"
del self.training_data_p1
del self.clsP1
print "Done deleting p1 set"
self.clsP2.train(self.training_data_p2)
def stripSmiles(self,text):
emos = [':)',':-)',';-)',': )',':d','=)',':p',';)','<3',':(',':-(',': (']
for item in emos:
text = text.replace(item,"")
return text
def stats(self,lang,mood):
if not self.statsData.has_key(lang):
self.statsData[lang] = {'n':0,'p':0}
if self.limit.has_key(lang):
limit = self.limit[lang]
else:
limit = self.limit['default']
if self.statsData[lang][mood] >= limit:
return 0
else:
self.statsData[lang][mood]+=1
return 1
def checkWithSentiwordnet(self, text):
tokens = nltk.word_tokenize(text)
for token in tokens:
synsets = self.swn.senti_synsets(token)
if len(synsets) > 0:
synset = self.swn.senti_synset(str(synsets[0]))
print synset
def checkKeyWords(self,text):
count = self.containsPositiveWord(text) + self.containsNegativeWord(text);
if count > 0:
return 'p'
if count < 0:
return 'n'
return 'x'
def containsPositiveWord(self,text):
count = 0
for item in dictionary.positive:
if item in text:
count += 1
#print 'p:',item
return count
def containsNegativeWord(self,text):
count = 0
for item in dictionary.negative:
if item in text:
#print 'n:', item
count -= 1
return count
def classifiyRaw(self,file,stripSmiles):
while True:
try:
tweet = cPickle.load(file)
except EOFError:
print "done classify"
break
except:
print "error"
pass
if self.skip > 0:
print "skip"
self.skip -= 1
continue
if tweet:
text = unicode(tweet.get('text'))
if text.lower().find('rt ') != -1:
print 'rt'
continue
mood = self.checkKeyWords(text)
if mood == 'x':
continue
lang = self.langClassifier.detect(text)
if stripSmiles:
text = self.stripSmiles(text)
sres = self.stats(lang[0], mood)
if sres == 0:
# limite de idioma alcanzado
print 'limit reached for ' , lang[0]
continue
if sres == -1:
print "done for %s" % mood
break
if self.count and self.count % 100 == 0:
print "classified %d tweets" % (self.count)
self.count += 1
self.checkWithSentiwordnet(text)
self.training_data_p1.addRow(text, mood, lang[0])
def countRows(self,file):
rows = 0
breakes = 0
while True:
try:
tweet = cPickle.load(file)
rows +=1
except EOFError:
break
except:
breakes +=1
print 'tweets:',rows,' breakes:',breakes
class RawClassifier(object):
statsData = {}
dataDir = "/home/toni/git/financial-twitter-sentiment-analyzer/tracker/data"
limit = {}
skip = 0
p2_f_limit = 0.75
def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.limit['en'] = 150000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/SentiWordNet_3.0.0_20100705.txt'
self.swn = SentiWordNetCorpusReader(swn_filename)
def classifyP1(self, stripSmiles=False):
self.classifiyRaw(self.tweetsFile, stripSmiles)
self.clsP1.train(self.training_data_p1)
print "done training P1"
print self.statsData
def classifyP2(self):
"""
remove noisy n-grams
"""
_st = {'tf': 0, 'df': 0}
for feutures, label in self.training_data_p1:
lang = feutures.pop('x_lang')
feuturesP2 = feutures.copy()
for f, v in feutures.items():
prob = self.clsP1.classifier.prob_classify({
f: v,
'x_lang': lang
})
_st['tf'] += 1
if max(prob.prob('n'), prob.prob('p')) <= self.p2_f_limit:
del feuturesP2[f]
_st['df'] += 1
if len(feuturesP2) >= 3:
feuturesP2['x_lang'] = lang
self.training_data_p2.append((feuturesP2, label))
else:
pass
print 'p2_length:', len(self.training_data_p2), ' p1_lenght:', len(
self.training_data_p1)
print 'st:', _st
print "deleting p1 set"
del self.training_data_p1
del self.clsP1
print "Done deleting p1 set"
self.clsP2.train(self.training_data_p2)
def stripSmiles(self, text):
emos = [
':)', ':-)', ';-)', ': )', ':d', '=)', ':p', ';)', '<3', ':(',
':-(', ': ('
]
for item in emos:
text = text.replace(item, "")
return text
def stats(self, lang, mood):
if not self.statsData.has_key(lang):
self.statsData[lang] = {'n': 0, 'p': 0}
if self.limit.has_key(lang):
limit = self.limit[lang]
else:
limit = self.limit['default']
if self.statsData[lang][mood] >= limit:
return 0
else:
self.statsData[lang][mood] += 1
return 1
def checkWithSentiwordnet(self, text):
tokens = nltk.word_tokenize(text)
for token in tokens:
synsets = self.swn.senti_synsets(token)
if len(synsets) > 0:
synset = self.swn.senti_synset(str(synsets[0]))
print synset
def checkKeyWords(self, text):
count = self.containsPositiveWord(text) + self.containsNegativeWord(
text)
if count > 0:
return 'p'
if count < 0:
return 'n'
return 'x'
def containsPositiveWord(self, text):
count = 0
for item in dictionary.positive:
if item in text:
count += 1
#print 'p:',item
return count
def containsNegativeWord(self, text):
count = 0
for item in dictionary.negative:
if item in text:
#print 'n:', item
count -= 1
return count
def classifiyRaw(self, file, stripSmiles):
while True:
try:
tweet = cPickle.load(file)
except EOFError:
print "done classify"
break
except:
print "error"
pass
if self.skip > 0:
print "skip"
self.skip -= 1
continue
if tweet:
text = unicode(tweet.get('text'))
if text.lower().find('rt ') != -1:
print 'rt'
continue
mood = self.checkKeyWords(text)
if mood == 'x':
continue
lang = self.langClassifier.detect(text)
if stripSmiles:
text = self.stripSmiles(text)
sres = self.stats(lang[0], mood)
if sres == 0:
# limite de idioma alcanzado
print 'limit reached for ', lang[0]
continue
if sres == -1:
print "done for %s" % mood
break
if self.count and self.count % 100 == 0:
print "classified %d tweets" % (self.count)
self.count += 1
self.checkWithSentiwordnet(text)
self.training_data_p1.addRow(text, mood, lang[0])
def countRows(self, file):
rows = 0
breakes = 0
while True:
try:
tweet = cPickle.load(file)
rows += 1
except EOFError:
break
except:
breakes += 1
print 'tweets:', rows, ' breakes:', breakes