# coding:utf-8
import json
from sklearn.externals import joblib
import numpy as np
import numpy.linalg as la
POSITIVE = 1 # 正面词语
NEGATIVE = 2 # 反面词语
NEUTRAL = 3 # 客观词语
def load_key_words(file_path):
with open(file_path, encoding="utf-8") as fp:
lines = fp.readlines()
lines = [line.replace("\n", "") for line in lines]
return lines
class DateSet:
def __init__(self, data, label):
self.data = np.array(data)
self.label = label
def Data(self):
return self.data
def Label(self):
return self.label
def load_date_sets(file_path):
with open(file_path, encoding="utf-8") as f:
data_list = json.load(f)
temp = []
for data in data_list:
# 前面是特征向量，后面最后一个是标签
label = data[-1]
feature = data[:-1]
d = DateSet(feature, label)
temp.append(d)
return temp
# 欧式距离,1表示100%，越接近0表示越不相似
def _ecl_sim(inA, inB):
return 1.0 / (1.0 + la.norm(inA - inB))
# 皮尔逊相关系数,范围-1->+1， 越大越相似
def _pears_sim(inA, inB):
if len(inA) < 3:
return 1.0
return 0.5 + 0.5 * np.corrcoef(inA, inB, rowvar=0)[0][1]
# 余弦相关范围-1->+1 越大越相似
def _cos_sim(inA, inB):
num = float(inB * inA.T)
de_nom = la.norm(inA) * la.norm(inB)
return 0.5 + 0.5 * (num / de_nom)
def _get_feature(sentence, key_word):
size = len(key_word)
feature = [0 for _ in range(size)]
for index in range(size):
word = key_word[index]
value = sentence.find(word) # 单词最初出现的位置
if value != -1:
feature[index] = 1
return np.array(feature)
def get_mood(sentence, key_word, model_name):
feature = _get_feature(sentence, key_word)
gnb = joblib.load(model_name)
pre_y = gnb.predict([feature])
result = {
"positive": 0,
"negative": 0,
"neutral": 0
}
try:
if pre_y[0] == POSITIVE:
result["positive"] = 1
elif pre_y[0] == NEGATIVE:
result["negative"] = 1
elif pre_y[0] == NEUTRAL:
result["neutral"] = 1
except:
pass
return result
