def calculate_score2(self, doc=""):
entities = self.webpageEntities(doc)
if len(entities) > 1:
uentities = {"Disaster": [], "LOCATION": [], "DATE": []}
#entityList = []
for sent in entities:
dictval = sent[1]
if dictval.has_key("Disaster"):
for k in dictval:
#entityList.extend(dictval[k])
if k in ["LOCATION", "Disaster", "DATE"]:
if uentities.has_key(k):
uentities[k].extend(dictval[k])
else:
uentities[k] = []
uentities[k].extend(dictval[k])
webpageEntities = []
for k in uentities:
temp = uentities[k]
ltext = " ".join(temp)
if k != "Disaster":
tokens = getTokenizedDoc(ltext)
else:
tokens = temp
webpageEntities.extend(tokens)
locs = set(tokens)
#tempList = [i for i in tempSet]
uentities[k] = [i for i in locs]
webpageEntitiesSet = set(webpageEntities)
#print webpageEntitiesSet
intersect = len(webpageEntitiesSet & self.entity_set)
score = intersect * 1.0 / len(self.entity_set)
#print intersect
if score < 0:
score = 0.0
else:
score = self.calculate_similarity(doc)
#score = 1.0/distance
return score
def calculate_score2(self,doc=""):
entities = self.webpageEntities(doc)
if len(entities) > 1:
uentities = {"Topic":[],"LOCATION":[],"DATE":[]}
#entityList = []
for sent in entities:
dictval = sent[1]
if dictval.has_key("Topic"):
for k in dictval:
#entityList.extend(dictval[k])
if k in ["LOCATION","Topic","DATE"]:
if uentities.has_key(k):
uentities[k].extend(dictval[k])
else:
uentities[k] = []
uentities[k].extend(dictval[k])
webpageEntities = []
for k in uentities:
temp = uentities[k]
ltext = " ".join(temp)
if k != "Topic":
tokens = getTokenizedDoc(ltext)
else:
tokens = temp
webpageEntities.extend(tokens)
locs = set(tokens)
#tempList = [i for i in tempSet]
uentities[k] = [i for i in locs]
webpageEntitiesSet = set(webpageEntities)
#print webpageEntitiesSet
intersect = len(webpageEntitiesSet & self.entity_set)
score = intersect * 1.0 / len(self.entity_set)
#print intersect
if score < 0:
score = 0.0
else:
score = self.calculate_similarity(doc)
#score = 1.0/distance
return score
def convertDoctoTFIDF(self,doc):
'''
stemmer = PorterStemmer()
tokenizer = WordPunctTokenizer()
stopwords_e = stopwords.words('english')
stopwords_e.extend(["last","time","week","favorite","home","search","follow","year","account","update","com","video","close","http","retweet","tweet","twitter","news","people","said","comment","comments","share","email","new","would","one","world"])
tokens = tokenizer.tokenize(doc)
clean = [token for token in tokens if token.isalnum()]
clean = [token.lower() for token in clean if token.lower() not in stopwords_e]
clean = [token for token in clean if len(token) > 2]
final_doc = [stemmer.stem(word) for word in clean]
'''
final_doc = getTokenizedDoc(doc)
doc_tfidf=[]
words = self.model[1]
for i in range(0,len(words)):
tf = final_doc.count(words[i])
if tf > 0:
tf = 1 + math.log(tf)
doc_tfidf.append((tf,words[i]))
return doc_tfidf