akshare股市新闻情绪判断

1

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

# -*- coding: utf-8 -*- import time import akshare as ak from snownlp import SnowNLP   # 使用snownlp stock_code = '603777' date = time.strftime("%Y%m%d", time.localtime()) stock_news_em_df = ak.stock_news_em(stock=stock_code)   for i in stock_news_em_df.values[:, 1]:     text=str(i)     # text = u'中国人是好人'     s = SnowNLP(text)     for sentence in s.sentences:         print(sentence, SnowNLP(sentence).sentences)     print(s.sentiments)     print(s.keywords(3))     print(s.summary(3))     # 小于0.4的为消极，否则为积极     if s.sentiments<0.4:         print('##########消极',i)     elif s.sentiments>=0.4:         print('##########积极',i)

 2

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

# 使用nltk # from nltk.sentiment.vader import SentimentIntensityAnalyzer as sia # import nltk # import time # import akshare as ak # import jieba as jb # # # nltk.set_proxy('SYSTEM PROXY') # # nltk.download('vader_lexicon') # # stock_code='603777' # date=time.strftime("%Y%m%d", time.localtime()) # # sentences = ['This is the worst lunch I ever had!', # #              'This is the best lunch I have ever had!!', # #              'I don\'t like this lunch.', # #              'I eat food for lunch.', # #              'Red is a color.', # #              'A really bad, horrible book, the plot was .'] # # '''每日快讯''' # # stock_zh_a_alerts_cls_df = ak.stock_zh_a_alerts_cls() # # '''当日最近 4 小时内的新闻资讯数据''' # # js_news_df = ak.js_news(timestamp=date + "11:27:18") # '''个股当日最近 20 条新闻资讯数据''' # stock_news_em_df = ak.stock_news_em(stock=stock_code) # sentences=[] # for i in stock_news_em_df.values[:,1]: #     seg_list = jb.cut_for_search(i) #     print(", ".join(seg_list)) #     sentences.append(", ".join(seg_list)) # hal = sia() # for sentence in sentences: #     print(sentence) #     ps = hal.polarity_scores(sentence) #     for k in sorted(ps): #         print('\t{}: {:>1.4}'.format(k, ps[k]), end='  ') #     print()

 3

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

# 使用fair # from flair.models import TextClassifier # from flair.data import Sentence # # sia = TextClassifier.load('en-sentiment') # # # def flair_prediction(x): #     sentence = Sentence(x) #     sia.predict(sentence) #     score = sentence.labels[0] #     if "POSITIVE" in str(score): #         return "pos" #     elif "NEGATIVE" in str(score): #         return "neg" #     else: #         return "neu" # # flair_prediction('hahahahah')