# -*- coding: utf-8 -*-
"""
author = 'suxuer'
date =
"""
import jieba
import jieba.posseg as pseg
import sys
import importlib
print("加载用户词典...")
importlib.reload(sys)
#sys.setdefaultencoding('utf8') #py没有这个
#下载需要分词的文件
jieba.load_userdict('G:/project/python_pachong/src/emotion_dict/pos_all_dict.txt')
jieba.load_userdict('G:/project/python_pachong/src/emotion_dict/neg_all_dict.txt')
#分词，返回List
def segmentation(sentence):
seg_list = jieba.cut(sentence)
seg_result = []
for word in seg_list:
seg_result.append(word)
return seg_result
# 分词，词性标注，词和词性构成一个元组
def postagger(sentence):
pos_data = pseg.cut(sentence)
pos_list = []
for w in pos_data:
pos_list.append((w.word, w.flag))
print (pos_list[:])
return pos_list
# 句子切分
def cut_sentence(words):
#words = words.decode('utf8')
start = 0
i = 0
token = 'meaningless'
sents = []
#根据标点符号将句子切分
punt_list = ',.!?;~，。！？；～… ' #.decode('utf8')
#print "punc_list", punt_list
for word in words:
#print "word", word
if word not in punt_list: # 如果不是标点符号
#print "word1", word
i += 1
token = list(words[start:i+2]).pop()
#print "token:", token
elif word in punt_list and token in punt_list: # 处理省略号
#print "word2", word
i += 1
token = list(words[start:i+2]).pop()
#print "token:", token
else:
#print "word3", word
sents.append(words[start:i+1]) # 断句
start = i + 1
i += 1
if start < len(words): # 处理最后的部分
sents.append(words[start:])
return sents
def read_lines(filename):
fp = open(filename, 'r',encoding='UTF-8')
lines = []
for line in fp.readlines():
line = line.strip()
line = line #.decode("utf-8")
lines.append(line)
fp.close()
return lines
# 去除停用词
def del_stopwords(seg_sent):
stopwords = read_lines("G://project/python_pachong/src/emotion_dict/stop_suxue.txt") # 读取停用词表
new_sent = [] # 去除停用词后的句子
for word in seg_sent:
if word in stopwords:
continue
else:
new_sent.append(word)
return new_sent
# 获取六种权值的词，根据要求返回list，这个函数是为了配合Django的views下的函数使用
def read_quanzhi(request):
result_dict = []
if request == "one":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/most.txt")
elif request == "two":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/very.txt")
elif request == "three":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/more.txt")
elif request == "four":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/ish.txt")
elif request == "five":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/insufficiently.txt")
elif request == "six":
result_dict = read_lines("G://project/python_pachong/src/degree_dict/inverse.txt")
else:
pass
return result_dict
#
# if __name__ == '__main__':
# test_sentence1 = "这款手机大小合适。"
# test_sentence2 = "这款手机大小合适，配置也还可以，很好用，只是屏幕有点小。。。总之，戴妃+是一款值得购买的智能手机。"
# test_sentence3 = "这手机的画面挺好，操作也比较流畅。不过拍照真的太烂了！系统也不好。"
# seg_result = segmentation(test_sentence3) # 分词，输入一个句子，返回一个list
# for w in seg_result:
# print (w)
# print ('\n')
# """
# """
# new_seg_result = del_stopwords(seg_result) # 去除停用词
# for w in new_seg_result:
# print (w)
#
# postagger(test_sentence1) # 分词，词性标注，词和词性构成一个元组
# cut_sentence(test_sentence2) # 句子切分
# lines = read_lines("G://project/python_pachong/src/test_data.txt")
# print (lines[:])
# -*- coding: utf-8 -*-
__author__ ="suxue"
"""
author = 'Su Xue'
date = 2018/7/3
"""
import text_process as tp
import numpy as np
# 1.读取情感词典和待处理文件
# 情感词典
print ("reading...")
posdict = tp.read_lines("G:/project/python_pachong/src/emotion_dict/pos_all_dict.txt")
negdict = tp.read_lines("G:/project/python_pachong/src/emotion_dict/neg_all_dict.txt")
# 程度副词词典
mostdict = tp.read_lines('G://project/python_pachong/src/degree_dict/most.txt') # 权值为2
verydict = tp.read_lines('G://project/python_pachong/src/degree_dict/very.txt') # 权值为1.5
moredict = tp.read_lines('G://project/python_pachong/src/degree_dict/more.txt') # 权值为1.25
ishdict = tp.read_lines('G://project/python_pachong/src/degree_dict/ish.txt') # 权值为0.5
insufficientdict = tp.read_lines('G://project/python_pachong/src/degree_dict/insufficiently.txt') # 权值为0.25
inversedict = tp.read_lines('G://project/python_pachong/src/degree_dict/inverse.txt') # 权值为-1
# 情感级别
emotion_level1 = "悲伤。在这个级别的人过的是八辈子都懊丧和消沉的生活。这种生活充满了对过去的懊悔、自责和悲恸。在悲伤中的人，看这个世界都是灰黑色的。"
emotion_level2 = "愤怒。如果有人能跳出冷漠和内疚的怪圈，并摆脱恐惧的控制，他就开始有欲望了，而欲望则带来挫折感，接着引发愤怒。愤怒常常表现为怨恨和复仇心里，它是易变且危险的。愤怒来自未能满足的欲望，来自比之更低的能量级。挫败感来自于放大了欲望的重要性。愤怒很容易就导致憎恨，这会逐渐侵蚀一个人的心灵。"
emotion_level3 = "淡定。到达这个能级的能量都变得很活跃了。淡定的能级则是灵活和无分别性的看待现实中的问题。到来这个能级，意味着对结果的超然，一个人不会再经验挫败和恐惧。这是一个有安全感的能级。到来这个能级的人们，都是很容易与之相处的，而且让人感到温馨可靠,这样的人总是镇定从容。他们不会去强迫别人做什么。"
emotion_level4 = "平和。他感觉到所有的一切都生机勃勃并光芒四射，虽然在其他人眼里这个世界还是老样子，但是在这人眼里世界却是一个。所以头脑保持长久的沉默，不再分析判断。观察者和被观察者成为同一个人，观照者消融在观照中，成为观照本身。"
emotion_level5 = "喜悦。当爱变得越来越无限的时候，它开始发展成为内在的喜悦。这是在每一个当下，从内在而非外在升起的喜悦。这个能级的人的特点是，他们具有巨大的耐性，以及对一再显现的困境具有持久的乐观态度，以及慈悲。同时发生着。在他们开来是稀松平常的作为，却会被平常人当成是奇迹来看待。"
# 情感波动级别
emotion_level6 = "情感波动很小，个人情感是不易改变的、经得起考验的。能够理性的看待周围的人和事。"
emotion_level7 = "情感波动较大，周围的喜悦或者悲伤都能轻易的感染他，他对周围的事物有敏感的认知。"
# 2.程度副词处理，根据程度副词的种类不同乘以不同的权值
def match(word, sentiment_value):
if word in mostdict:
sentiment_value *= 2.0
elif word in verydict:
sentiment_value *= 1.75
elif word in moredict:
sentiment_value *= 1.5
elif word in ishdict:
sentiment_value *= 1.2
elif word in insufficientdict:
sentiment_value *= 0.5
elif word in inversedict:
#print "inversedict", word
sentiment_value *= -1
return sentiment_value
# 3.情感得分的最后处理，防止出现负数
# Example: [5, -2] → [7, 0]; [-4, 8] → [0, 12]
def transform_to_positive_num(poscount, negcount):
pos_count = 0
neg_count = 0
if poscount < 0 and negcount >= 0:
neg_count += negcount - poscount
pos_count = 0
elif negcount < 0 and poscount >= 0:
pos_count = poscount - negcount
neg_count = 0
elif poscount < 0 and negcount < 0:
neg_count = -poscount
pos_count = -negcount
else:
pos_count = poscount
neg_count = negcount
return (pos_count, neg_count)
# 求单条微博语句的情感倾向总得分
def single_review_sentiment_score(weibo_sent):
single_review_senti_score = []
cuted_review = tp.cut_sentence(weibo_sent) # 句子切分，单独对每个句子进行分析
for sent in cuted_review:
seg_sent = tp.segmentation(sent) # 分词
seg_sent = tp.del_stopwords(seg_sent)[:]
#for w in seg_sent:
# print w,
i = 0 # 记录扫描到的词的位置
s = 0 # 记录情感词的位置
poscount = 0 # 记录该分句中的积极情感得分
negcount = 0 # 记录该分句中的消极情感得分
for word in seg_sent: # 逐词分析
#print word
if word in posdict: # 如果是积极情感词
#print "posword:", word
poscount += 1 # 积极得分+1
for w in seg_sent[s:i]:
poscount = match(w, poscount)
#print "poscount:", poscount
s = i + 1 # 记录情感词的位置变化
elif word in negdict: # 如果是消极情感词
#print "negword:", word
negcount += 1
for w in seg_sent[s:i]:
negcount = match(w, negcount)
#print "negcount:", negcount
s = i + 1
# 如果是感叹号，表示已经到本句句尾
#elif word == "！" :
elif word == "！" or word == "!":
for w2 in seg_sent[::-1]: # 倒序扫描感叹号前的情感词，发现后权值+2，然后退出循环
if w2 in posdict:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
#print "poscount,negcount", poscount, negcount
single_review_senti_score.append(transform_to_positive_num(poscount, negcount)) # 对得分做最后处理
pos_result, neg_result = 0, 0 # 分别记录积极情感总得分和消极情感总得分
for res1, res2 in single_review_senti_score: # 每个分句循环累加
pos_result += res1
neg_result += res2
#print pos_result, neg_result
result = pos_result - neg_result # 该条微博情感的最终得分
result = round(result, 1)
return result
# 测试
if __name__=='__main__':
weibo_sent = "您好，公司跨境出口和跨境进口业务与阿里旗下平台均有合作。感谢您对公司的关注"
score = single_review_sentiment_score(weibo_sent)
print (score)