class Analyzer(object):
def __init__(self):
self.mwnet = MWNet(os.path.join("data", "mwnet.db"))
self.swn = SentiWordNetCorpusReader(os.path.join("data", "SentiWordNet_3.0.0.txt"))
if os.getenv('TANL_EMAIL'):
self.splitter = TanlSplitter()
else:
self.splitter = SimpleSplitter()
def analyze_sentence(self, sentence):
scores = []
result = {}
for word, lemma, tag, wn_type, indices in self.splitter.iter_words(sentence):
# Here we can also impose the type to be an ADJ, NAME, or VERB
synsets = self.mwnet.get_english_synsets(lemma, wn_type)
if not synsets:
continue
synsets_dict = {}
found = False
for syn in synsets:
for translation in self.mwnet.get_translation(syn):
senti_synsets = self.swn.senti_synsets(translation, wn_type)
if not senti_synsets:
continue
synsets_dict[syn] = map(lambda x: [x.synset.name, x.pos_score, x.neg_score, x.obj_score], senti_synsets)
scores.extend(map(lambda x: (x.pos_score, x.neg_score, x.obj_score), senti_synsets))
if found:
break
positive = map(lambda x: x[0], scores)
negative = map(lambda x: x[1], scores)
objective = map(lambda x: x[2], scores)
if len(positive) > 0:
pscore = sum(positive) * 1.0 / len(positive)
nscore = sum(negative) * 1.0 / len(positive)
oscore = sum(objective) * 1.0 / len(positive)
result[word] = {
'indices': indices,
'lemma': lemma,
'features': tag,
'synsets': synsets_dict,
'scores': {
'positive': pscore,
'negative': nscore,
'objective': oscore,
},
}
return result
def __init__(self):
self.mwnet = MWNet(os.path.join("data", "mwnet.db"))
self.swn = SentiWordNetCorpusReader(os.path.join("data", "SentiWordNet_3.0.0.txt"))
if os.getenv('TANL_EMAIL'):
self.splitter = TanlSplitter()
else:
self.splitter = SimpleSplitter()
