def get_sentics_of_sentence(self, sentence):
words = sentence.split()
list_concepts = []
conc = []
to_add = ""
for word in words:
if (word in self.G):
conc.append(word)
to_add += word + " "
elif (to_add != ""):
list_concepts.append(to_add[:-1])
to_add = ""
if (to_add != ""):
list_concepts.append(to_add[:-1])
parserList = SenticParser.getOutputConcepts(sentence)
list_concept = list(set(list_concepts) | set(parserList))
list_concept = filter(bool, list_concept)
list_concept = set(list(list_concepts))
to_search = []
for phrase in list_concepts:
concepts = phrase.split()
to_search = to_search + concepts
for i in range(len(concepts) - 1):
for j in range(i + 1, len(concepts)):
try:
k = nx.dijkstra_path(self.G, concepts[i], concepts[j])
if (len(k) == j - i + 1 and k == concepts[i:j + 1]):
to_search = list(set(to_search) - set(k))
word_to_add = "_".join(k)
to_search.append(word_to_add)
except:
continue
to_search = list(set(to_search))
sorted_by_length = sorted(to_search,
key=lambda tup: len(tup.split("_")))
return filter(lambda x: x is not None,
[self.sn.lookup(concept) for concept in to_search])
def get_sentics_of_sentence(self, sentence):
words = sentence.split()
list_concepts = []
conc = []
to_add = ""
for word in words:
if (word in self.G):
conc.append(word)
to_add += word + " "
elif(to_add != ""):
list_concepts.append(to_add[:-1])
to_add = ""
if(to_add != ""):
list_concepts.append(to_add[:-1])
parserList = SenticParser.getOutputConcepts(sentence)
list_concept = list(set(list_concepts) | set(parserList))
list_concept = filter(bool, list_concept)
list_concept = set(list(list_concepts))
to_search = []
for phrase in list_concepts:
concepts = phrase.split()
to_search = to_search + concepts
for i in range(len(concepts) - 1):
for j in range(i + 1, len(concepts)):
try:
k = nx.dijkstra_path(self.G, concepts[i], concepts[j])
if(len(k) == j - i + 1 and k == concepts[i:j + 1]):
to_search = list(set(to_search) - set(k))
word_to_add = "_".join(k)
to_search.append(word_to_add)
except:
continue
to_search = list(set(to_search))
sorted_by_length = sorted(to_search, key=lambda tup:len(tup.split("_")))
return filter(lambda x: x is not None, [self.sn.lookup(concept) for concept in to_search])
if (word in G):
conc.append(word)
to_add = to_add + word + " "
#print to_add
else:
if (to_add != ""):
list_concepts.append(to_add[:-1])
to_add = ""
if (to_add != ""):
list_concepts.append(to_add[:-1])
print list_concepts
parserList = SenticParser.getOutputConcepts(sentence)
print parserList
list_concept = list(set(list_concepts) | set(parserList))
list_concept = filter(bool, list_concept)
list_concept = set(list(list_concepts))
sn = senticnet.Senticnet()
to_search = []
for phrase in list_concepts:
concepts = phrase.split()
to_search = to_search + concepts
conc.append(word)
to_add = to_add + word + " "
# print to_add
else:
if to_add != "":
list_concepts.append(to_add[:-1])
to_add = ""
if to_add != "":
list_concepts.append(to_add[:-1])
print list_concepts
parserList = SenticParser.getOutputConcepts(sentence)
print parserList
list_concept = list(set(list_concepts) | set(parserList))
list_concept = filter(bool, list_concept)
list_concept = set(list(list_concepts))
sn = senticnet.Senticnet()
to_search = []
for phrase in list_concepts:
concepts = phrase.split()
def process_sentence(sentence):
G = nx.read_gpickle( "test.gpickle" )
sentence = sentence.lower()
bigrams = []
words = sentence.split()
list_concepts = []
conc = []
to_add = ""
for word in words:
if ( word in G ):
conc.append(word)
to_add = to_add+ word+" "
#print to_add
else:
if( to_add != "" ):
list_concepts.append(to_add[:-1])
to_add = ""
if( to_add != "" ):
list_concepts.append(to_add[:-1])
#print "list of concepts"
#print list_concepts
parserList = SenticParser.getOutputConcepts(sentence)
#print "parseList"
#print parserList
list_concept = list( set(list_concepts) | set(parserList) )
list_concept = filter(bool, list_concept)
list_concept = set(list(list_concepts))
sn = senticnet.Senticnet()
to_search = []
for phrase in list_concepts:
concepts = phrase.split()
to_search = to_search + concepts
for i in range(len(concepts) - 1):
for j in range(i+1, len(concepts)):
try:
k = nx.dijkstra_path(G,concepts[i], concepts[j])
#print k
if( len(k) == j-i+1 and k == concepts[i:j+1] ):
to_search = list( set(to_search) - set(k) )
word_to_add = "_".join(k)
to_search.append( word_to_add )
except:
continue
to_search = list( set( to_search ) )
sorted_by_length = sorted(to_search, key=lambda tup:len(tup.split("_")) )
#print "sorted by length"
#print sorted_by_length
#print "to search"
#print to_search
# My features accumulated
pos=0
neg=0
pleasan=0
atten=0
sensi=0
apti=0
for concept in to_search:
try:
# print concept
# print "---------------------------"
# print sn.concept( concept)
pol=sn.polarity( concept )
if(pol>0):
pos += pol
else:
neg += pol
# print sn.semantics( concept )
# print sn.sentics(concept)
sentics = sn.sentics(concept)
pleasan += sentics.get("pleasantness")
atten += sentics.get("attention")
sensi += sentics.get("sensitivity")
apti += sentics.get("aptitude")
# print "==================================================================================="
except:
continue
result = {"positivity" : pos, "negativity": neg, "pleasantness" : pleasan,
"attention" : atten, "sensitivity" : sensi,
"aptitude" : apti}
return result