def get_sentiment(testtxt):
# 情感分析 example 1
xm = XmNLP(testtxt)
if xm.sentiment() < 0.2:
return 0
elif xm.sentiment() < 0.4:
return 1
elif xm.sentiment() < 0.6:
return 2
elif xm.sentiment() < 0.8:
return 3
else:
return 4
def get(self, text):
xm = XmNLP(text)
time_stamp = str(int(time.time()))
data = get_tc_res(text, time_stamp)
result = {'polar': 0, 'confd': 0, 'sentiment': 0}
result['sentiment'] = xm.sentiment()
if data['polar'] == 0:
result['polar'] = 1
else:
result['polar'] = data['polar']
# result['polar'] = data['polar'] if data['polar'] == 0 else 1
result['confd'] = data['confd']
return jsonify(result)
情感计算
/ naive bayes /
"""
print(descr)
doc = """真伤心"""
doc2 = """天气太好了，我们去钓鱼吧"""
print('\n++++++++++++++++++++++++ usage 1 ++++++++++++++++++++++++\n')
"""
1. 使用类来进行操作
"""
from xmnlp import XmNLP
xm = XmNLP(doc, stopword=True)
print('Text: ', doc)
print('Score: ', xm.sentiment())
print('Text: ', doc2)
print('Score: ', xm.sentiment(doc2))
print('\n++++++++++++++++++++++++ usage 2 ++++++++++++++++++++++++\n')
import xmnlp
print('Text: ', doc)
print('Score: ', xmnlp.sentiment(doc))
print('Text: ', doc2)
print('Score: ', xmnlp.sentiment(doc2))