class NaiveBayesAnalyzer:
def __init__(self, dict):
self._dict = dict
self._fe = FeatureExtractor()
def train(self):
train_data = []
for k, v in self._dict.items():
train_data = train_data + [
(self._fe.default_feature_extractor(f), k) for f in v
]
self._classifier = nltk.classify.NaiveBayesClassifier.train(train_data)
def analyze(self, text):
feats = self._fe.default_feature_extractor(text)
prob_dist = self._classifier.prob_classify(feats)
classification = prob_dist.max()
# print(classification)
# for k in self._dict.keys():
# print (k, prob_dist.prob(k))
return classification
