class SimpleNLP(object):
def __init__(self, method=1, doc=None, datalist=None):
self.doc = doc
self.datalist = datalist
self.seg = Seg()
self.sentiment = Sentiment(method)
self.method = method
def seg_datalist(self):
return self.seg.seg_from_datalist(self.datalist)
def seg_doc(self):
return self.seg.seg_from_doc(self.doc)
def get_keyword_datalist(self):
return dict(self.seg.get_keyword_from_datalist(self.datalist))
def sentiment_analysis_doc(self):
if self.method == 1:
self.sentiment.load_model(root_path +
'/data/naivebayes_model30000v3')
elif self.method == 2:
self.sentiment.load_model(root_path + '/data/svmmodel10000v4')
return self.sentiment.predict_sentence_doc(self.doc)
def sentiment_analysis_datalist(self):
if self.method == 1:
self.sentiment.load_model(root_path +
'/data/naivebayes_model30000v3')
elif self.method == 2:
self.sentiment.load_model(root_path + '/data/svmmodel10000v4')
return self.sentiment.predict_datalist(self.datalist)
class SVM(object):
def __init__(self, c, best_words):
self.seg = Seg()
self.clf = SVC(probability=True, C=c)
self.train_data = []
self.train_label = []
self.best_words = best_words
def words2vector(self, all_data):
vectors = []
for data in all_data:
vector = []
for feature in self.best_words:
vector.append(data.count(feature))
vectors.append(vector)
# print(vector)
vectors = np.array(vectors)
return vectors
def train_model(self, data):
print("------ SVM Classifier is training ------")
for d in data:
label = d[0]
doc = d[1]
self.train_data.append(doc)
self.train_label.append(label)
self.train_data = np.array(self.train_data)
self.train_label = np.array(self.train_label)
train_vectors = self.words2vector(self.train_data)
self.clf.fit(train_vectors, self.train_label)
print("------ SVM Classifier training over ------")
def save_model(self, filename):
print("------ SVM Classifier is saving model ------")
joblib.dump(self.clf, filename+'-model.m')
f = gzip.open(filename + '-bestwords.dat', 'wb')
d = {}
d['best words'] = self.best_words
f.write(pickle.dumps(d))
f.close()
print("------ SVM Classifier saving model over ------")
def load_model(self, filename):
print("------ SVM Classifier is loading model ------")
self.clf = joblib.load(filename+'-model.m')
f = gzip.open(filename+'-bestwords.dat', 'rb')
d = pickle.loads(f.read())
f.close()
self.best_words = d['best words']
print("------ SVM Classifier loading model over ------")
def predict_wordlist(self, sentence):
vector = self.words2vector([sentence])
prediction = self.clf.predict(vector)
prob = self.clf.predict_proba(vector)[0][1]
return prediction[0], prob
def predict_sentence(self, sentence):
seged_sentence = self.seg.seg_from_doc(sentence)
prediction, prob = self.predict_wordlist(seged_sentence)
return prediction, prob
def predict_datalist(self, datalist):
seged_datalist = self.seg.seg_from_datalist(datalist)
result = []
for data in seged_datalist:
prediction, prob = self.predict_wordlist(data)
result.append(prob)
return result
