def get(self):
# standardize
keywords = self.trimmed_stopwords(self.tokenize(self.theme, pos='noun_verbs'))
# search about theme
articles = self.search_articles([keyword.surface for keyword in keywords][:3])
# clean
docs = map(self.clean, articles)
# divide sentences
sentences_cand = map(self.divide, docs)
sent = []
for s in sentences_cand:
sent.append(list(filter(self.is_sentence, s)))
sentences = list(chain.from_iterable(sent))
# tfidf format
sentence_tokens = []
for sentence in sentences:
noun_tokens = [token.surface for token in self.tokenize(sentence, pos='noun')]
sentence_tokens.append(' '.join(noun_tokens))
# vectorize
vector = TfIdf.vector(sentence_tokens)
# clustering
cluster = numpy.array(TfIdf.cluster(vector, clusters=3))
# retrieve opinion with tf
tfidf_score_index = numpy.argsort(numpy.array([sum(v) for v in vector.toarray()]))[::-1]
opinions = []
for i in range(3):
# retrieve vector index by cluster
c_index = numpy.where(cluster == i)
for k in tfidf_score_index:
if k in c_index[0]:
opinions.append(sentences[k])
break
theme = namedtuple('Theme', 'keywords, opinions')
return theme(' '.join([keyword.surface for keyword in keywords][:3]), opinions)
def get(self):
# standardize
keywords = self.trimmed_stopwords(
self.tokenize(self.opinion, pos='noun_verbs'))
# search about opinion with keywords
articles = self.search_articles(
self.keywords + [keyword.surface for keyword in keywords][:3])
# clean
docs = map(self.clean, articles)
# divide sentences
sentences_cand = map(self.divide, docs)
sent = []
for s in sentences_cand:
sent.append(list(filter(self.is_sentence, s)))
sentences = list(chain.from_iterable(sent))
# tfidf format
sentence_tokens = []
for sentence in sentences:
noun_tokens = [
token.surface for token in self.tokenize(sentence, pos='noun')
]
sentence_tokens.append(' '.join(noun_tokens))
# vectorize
vector = TfIdf.vector(sentence_tokens)
# clustering
cluster = numpy.array(TfIdf.cluster(vector, clusters=3))
# retrieve opinion with tf
tfidf_score = numpy.array([sum(v) for v in vector.toarray()])
# retrieve opinion with senti
# senti_score = numpy.array([self.senti(s) for s in sentences])
senti_score = []
# for s in sentences:
# senti_score.append(self.senti(s))
for sentence in sentences:
senti_tokens = [
token.surface for token in self.tokenize(sentence, pos='senti')
]
senti_score.append(self.senti(senti_tokens))
senti_score = numpy.array(senti_score)
score_index = numpy.argsort(tfidf_score * senti_score)
positives = []
negatives = []
for i in range(3):
# retrieve vector index by cluster
c_index = numpy.where(cluster == i)
for k in score_index:
if k in c_index[0]:
negatives.append(sentences[k])
break
for k in score_index[::-1]:
if k in c_index[0]:
positives.append(sentences[k])
break
opinion = namedtuple('Opinion', 'positives, negatives')
return opinion(positives, negatives)
