def calculate(news_sent):
cuted_review = list(jieba.cut(news_sent)) # 句子切分，单独对每个句子进行分析
cuted_review = tp.del_stopwords(cuted_review)
print(cuted_review)
count_list = []
sent_list = []
for sent in cuted_review:
count_list = match(sent, count_list)
sent_list = sent_match(sent, sent_list)
score = mutiplication_list(count_list, sent_list)
if score != 0:
if ('只要' in cuted_review)|('虽然' in cuted_review)|('虽说' in cuted_review) | ('如果' in cuted_review) | ('即使' in cuted_review) | ('即便' in cuted_review) | (
'不要说只有' in cuted_review) | ('即便真的有' in cuted_review) | ('除非' in cuted_review) | ('否则' in cuted_review) | (
'若' in cuted_review):
score = 0
else:
if (('下降' in cuted_review)|('降低' in cuted_review)|('增加' in cuted_review)) & (('费用' in cuted_review)|('成本' in cuted_review)|('财务费用' in cuted_review)|('财务成本' in cuted_review)|('风险' in cuted_review)|('成本' in cuted_review)|('进口成本' in cuted_review)):
score=score*(-1)
elif ('房价' in cuted_review) & (('下降' in cuted_review)|('下跌' in cuted_review)):
score = score * (-1)
elif (('现货价格' in cuted_review) | ('油价' in cuted_review)) & (
('上涨' in cuted_review) | ('大涨' in cuted_review)):
score = score * (-1)
elif (('下降' in cuted_review) | ('回落' in cuted_review) | ('上行' in cuted_review) | (
'下行' in cuted_review)) & (('CPI' in cuted_review) |('通胀' in cuted_review) | ('商品价格' in cuted_review) | ('物价' in cuted_review) | (
'原油' in cuted_review) | ('利率' in cuted_review)|('成本' in cuted_review)):
score = score * (-1)
elif ('抵御住了' in cuted_review) & ('冲击' in cuted_review):
score = score * (-1)
elif ('下跌' in cuted_review) & ('反弹' in cuted_review):
score = score * (-1)
elif ('损失' in cuted_review) & (('减少' in cuted_review) | ('最小化' in cuted_review)):
score = score * (-1)
elif (('下跌风险' in cuted_review) | ('风险' in cuted_review)) & ('控制' in cuted_review):
score = score * (-1)
elif ('下降' in cuted_review) & ('比例' in cuted_review):
score = 0
elif ('压力' in cuted_review) & ('缓解' in cuted_review):
score = score * (-1)
elif ('至少要到' in cuted_review) & ('回升' in cuted_review):
score = score * (-1)
elif ('增强' in cuted_review) & ('忧虑' in cuted_review):
score = score * (-1)
elif (('下调' in cuted_review) | ('下降' in cuted_review)) & (('存准率' in cuted_review) | (
'存款准备金率' in cuted_review)|('基准利率' in cuted_review)|('CPI' in cuted_review)):
score = score * (-1)
elif ('扑灭' in cuted_review) & ('希望' in cuted_review):
score = score * (-1)
return score
def calculate(news_sent):
single_review_senti_score = []
cuted_review = list(jieba.cut(news_sent)) # 句子切分，单独对每个句子进行分析
cuted_review = tp.del_stopwords(cuted_review)
count_list = []
for sent in cuted_review:
count_list = match(sent, count_list)
score = mutiplication_list(count_list)
return score
def sentence_score(sentence):
final_score = []
cuted_review = tp.cut_sentence(sentence) #cut sentence into subsentences
# for w in cuted_review:
# print w
for sent in cuted_review:
seg_sent = tp.segmentation(sent) # segment words
seg_sent = tp.del_stopwords(seg_sent)[:]
# for w in seg_sent:
# print w
i = 0 # current location
s = 0 # emotion word location
poscount = 0 # positive word score
negcount = 0 # negative word score
for word in seg_sent:
# print word
if word in posdict:
# print word
poscount += 1
for w in seg_sent[s:i]:
# print w
poscount = match(w, poscount)
# print poscount
s = i + 1
elif word in negdict:
negcount += 1
for w in seg_sent[s:i]:
negcount = match(w, negcount)
s = i + 1
# if ! ！, which means coming to end of sentence
elif word == "！".decode("utf-8") or word == "!".decode('utf-8'):
for w2 in seg_sent[::-1]:
if w2 in posdict:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
final_score.append(transform_to_positive_num(poscount, negcount)) # final process
pos_result, neg_result = 0, 0
for res1, res2 in final_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 # final score
return result
def single_review_sentiment_score(comment_sent):
single_review_senti_score = []
cuted_review = tp.cut_sentence(comment_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 == "！" 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 # 该条评论情感的最终得分
result = round(result, 1)
return result
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 == "！".decode("utf-8") or word == "!".decode('utf-8'):
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
if v == 'inverse':
v = -1
score = score * v
else:
score = score * v
return score
else:
for i, v in enumerate(list):
if v == 'inverse':
v = -1
score = score * v
else:
score = score * v
return score
text='骑乘收益率曲线策略是短期货币市场证券管理中流行的一种策略。'
single_review_senti_score = []
cuted_review = list(jieba.cut(text)) # 句子切分，单独对每个句子进行分析
cuted_review = tp.del_stopwords(cuted_review)
print(cuted_review)
count_list = []
for sent in cuted_review:
count_list=match(sent,count_list)
score=mutiplication_list(count_list)
print(score)
import text_process as tp
import jieba
jieba.load_userdict(
'E:\postgraduate\\no_space_environment\category\pycharm\pycharm_file_location\\thesis\新闻文本处理\论文词典法CSDN\Sentiment_dict\emotion_dict\pos_all_dict.txt'
)
jieba.load_userdict(
'E:\postgraduate\\no_space_environment\category\pycharm\pycharm_file_location\\thesis\新闻文本处理\论文词典法CSDN\Sentiment_dict\emotion_dict\\neg_all_dict.txt'
)
jieba.load_userdict(
"E:\postgraduate\\no_space_environment\category\pycharm\pycharm_file_location\\thesis\新闻文本处理\论文词典法CSDN\Sentiment_dict\degree_dict\insufficiently_inverse.txt"
)
news_sent = '我不是很看好这支股票会上涨。'
# cuted_review = tp.cut_sentence(news_sent) # 句子切分，单独对每个句子进行分析
cuted_review = list(jieba.cut(news_sent))
print(cuted_review)
for sent in cuted_review:
seg_sent = tp.segmentation(sent) # 分词
seg_sent = tp.del_stopwords(seg_sent)[:]
print(seg_sent)
