class extractor(object):
def __init__(self):
self.path = config.basePath +config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
def load_obj(self,name):
with open( self.path + name + '.pkl', 'rb') as f:
return pickle.load(f)
def getSentimentScore(self,message):
message = self.filter.process(message)
emo = re.findall(r"(\w+)#\(([+-]*\d+.\d+)\)#",message.decode("utf-8"))
emoScore = 0.0
for e in emo:
emoScore += float(e[1])
# print(emo)
# remove emo info
onlyEMO = 0
message = re.sub(r"\w+#\([+-]*\d+.\d+\)#"," ",message.decode("utf-8"))
if len(message.strip()) == 0:
onlyEMO = 1
vec = self.vectorizer.transform([message.encode("utf-8")])
Tvec = self.ch2.transform(vec)
predScore = self.SentiModel.predict(Tvec)
# Vary weights if wanted (default - 0.35,0.65)
if onlyEMO == 1:
predScore = emoScore
elif len(emo) >= 1:
predScore = 0.35 * predScore + 0.65 * emoScore
return float(predScore)
class extractor(object):
def __init__(self):
self.path = config.basePath + config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
def load_obj(self, name):
with open(self.path + name + '.pkl', 'rb') as f:
return pickle.load(f)
def getSentimentScore(self, message):
message = self.filter.process(message)
emo = re.findall(r"(\w+)#\(([+-]*\d+.\d+)\)#", message.decode("utf-8"))
emoScore = 0.0
for e in emo:
emoScore += float(e[1])
# print(emo)
# remove emo info
onlyEMO = 0
message = re.sub(r"\w+#\([+-]*\d+.\d+\)#", " ",
message.decode("utf-8"))
if len(message.strip()) == 0:
onlyEMO = 1
vec = self.vectorizer.transform([message.encode("utf-8")])
Tvec = self.ch2.transform(vec)
predScore = self.SentiModel.predict(Tvec)
# Vary weights if wanted (default - 0.35,0.65)
if onlyEMO == 1:
predScore = emoScore
elif len(emo) >= 1:
predScore = 0.35 * predScore + 0.65 * emoScore
return float(predScore)
class extractor(object):
def __init__(self):
self.path = config.basePath + config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
def load_obj(self, name):
with open(self.path + name + '.pkl', 'rb') as f:
return pickle.load(f)
def simpleProcess(self, text):
text = text.lower().strip()
line = re.sub(Url_RE, "", text)
line = re.sub(r"[@#]", "", line)
line = u" ".join(tokenize(line))
return line
def getSentimentScore(self, message):
vec = self.vectorizer.transform([self.simpleProcess(message)])
Tvec = self.ch2.transform(vec)
plainScore = self.SentiModel.predict(Tvec)
#####
message = self.filter.process(message)
emo = re.findall(r"(\w+)#\(([+-]*\d+.\d+)\)#", message.decode("utf-8"))
emoScore = 0.0
for e in emo:
emoScore += float(e[1])
# remove emo info
onlyEMO = 0
message = re.sub(r"\w+#\([+-]*\d+.\d+\)#", " ",
message.decode("utf-8"))
if len(message.strip()) == 0:
onlyEMO = 1
vec = self.vectorizer.transform([message])
Tvec = self.ch2.transform(vec)
predScore = self.SentiModel.predict(Tvec)
# Vary weights if wanted
if onlyEMO == 1:
predScore = emoScore
elif len(emo) >= 1:
predScore = 0.20 * predScore + 0.30 * plainScore + 0.50 * emoScore
else:
predScore = 0.35 * predScore + 0.65 * plainScore
return float(predScore)
class extractor(object):
def __init__(self):
self.path = config.basePath +config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
def load_obj(self,name):
with open( self.path + name + '.pkl', 'rb') as f:
return pickle.load(f)
def simpleProcess(self,text):
text = text.lower().strip()
line = re.sub(Url_RE,"",text)
line = re.sub(r"[@#]","",line)
line =u" ".join(tokenize(line))
return line
def getSentimentScore(self,message):
vec = self.vectorizer.transform([self.simpleProcess(message)])
Tvec = self.ch2.transform(vec)
plainScore = self.SentiModel.predict(Tvec)
#####
message = self.filter.process(message)
emo = re.findall(r"(\w+)#\(([+-]*\d+.\d+)\)#",message.decode("utf-8"))
emoScore = 0.0
for e in emo:
emoScore += float(e[1])
# remove emo info
onlyEMO = 0
message = re.sub(r"\w+#\([+-]*\d+.\d+\)#"," ",message.decode("utf-8"))
if len(message.strip()) == 0:
onlyEMO = 1
vec = self.vectorizer.transform([message])
Tvec = self.ch2.transform(vec)
predScore = self.SentiModel.predict(Tvec)
# Vary weights if wanted
if onlyEMO == 1:
predScore = emoScore
elif len(emo) >= 1:
predScore = 0.20 * predScore + 0.30 * plainScore + 0.50 * emoScore
else:
predScore = 0.35 * predScore + 0.65 * plainScore
return float(predScore)
def __init__(self):
self.path = config.basePath + config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
def __init__(self):
self.path = config.basePath +config.microblog
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.filter = Filter()
