125 lines
3.9 KiB
Python
125 lines
3.9 KiB
Python
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from utils.comm import *
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from snownlp import SnowNLP
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import jieba
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import jieba.posseg as pseg
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import jieba.analyse
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class News:
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def __init__(self, trade_date=datetime.now()):
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self.trade_date = trade_date.strftime('%Y%m%d')
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@staticmethod
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def preprocess(text):
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seg_list = jieba.lcut(text) # 文本预处理及分词
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return seg_list
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@staticmethod
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def sentiment_analysis(text): # 情感分析
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s = SnowNLP(text)
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sentiment = s.sentiments # 情感得分，范围0-1，越接近1表示正面情感，越接近0表示负面情感
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return sentiment
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@staticmethod
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def extract_keywords(text, top_k=5): # 关键词提取
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keywords = jieba.analyse.extract_tags(text, topK=top_k, withWeight=False, allowPOS=()) # 提取关键词，默认返回前5个
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return keywords
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@staticmethod
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def named_entity_recognition(text): # 命名实体识别
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words = pseg.cut(text)
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entities = []
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for word, flag in words:
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# 识别人名、地名、机构名、其它专名
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if flag in ['nr', 'ns', 'nt', 'nz']:
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entities.append((word, flag))
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return entities
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def result(self, news):
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print("原文：", news)
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preprocessed_news = self.preprocess(news)
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sentiment_score = self.sentiment_analysis(news)
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keywords = self.extract_keywords(news, top_k=3)
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entities = self.named_entity_recognition(news)
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print("分词结果：", preprocessed_news)
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print("情感分析得分：", round(sentiment_score, 2))
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print("关键词提取：", keywords)
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print("命名实体识别：", entities)
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def news_cctv(self):
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for _ in range(5):
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try:
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# 中央新闻
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df = ak.news_cctv(date=self.trade_date)
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# 情感分析
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# df['title'].apply(lambda x: self.result(x))
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# print(df['情感'])
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return df
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except Exception as e:
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print(f"{self.trade_date} 日的新闻咨询拉取发生错误！{e.__traceback__}")
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time.sleep(1)
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continue
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else:
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print("中央新闻 5次出现错误，请关注！！！")
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return None
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def news_cls(self):
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for _ in range(5):
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try:
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# 财联社-电报
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df = ak.stock_telegraph_cls()
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# 情感分析
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# df['title'].apply(lambda x: self.result(x))
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# print(df['情感'])
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return df
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except Exception as e:
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print(f"{self.trade_date} 财联社-电报 咨询拉取发生错误！{e.__traceback__}")
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continue
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else:
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print("财联社-电报 5次出现错误，请关注！！！")
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return None
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@staticmethod
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def news_stock_by_code(symbol, date_time=None):
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# 个股新闻
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stock_news_em_df = ak.stock_news_em(symbol=symbol)
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# 将发布时间列转换为日期时间类型
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stock_news_em_df['发布时间'] = pd.to_datetime(stock_news_em_df['发布时间'])
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# print(stock_news_em_df)
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if date_time is not None:
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if len(date_time.split("-")) == 1:
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str_date = datetime.strptime(str(date_time), '%Y%m%d').strftime('%Y-%m-%d')
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else:
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str_date = datetime.strptime(str(date_time), '%Y-%m-%d').strftime('%Y-%m-%d')
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else:
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# 获取今天的日期
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str_date = datetime.now().date()
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# 使用日期过滤器筛选出今天的新闻
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today_news = stock_news_em_df[stock_news_em_df['发布时间'].dt.date == str_date]
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# 打印今天的新闻
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print(today_news['新闻标题'])
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print(today_news["新闻内容"])
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# today_news['新闻标题'].apply(lambda x: result(x))
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return today_news
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def html_page_data(self):
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cctv_df = self.news_cctv()
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cls_df = self.news_cls()
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cctv_content, cls_content = '', ''
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for index, row in cctv_df.iterrows():
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cctv_content += f'<tr><th scope="row">{row["title"]}</th>' \
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f'<td>开发中...</td>'
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cctv_content += '</tr>'
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for index, row in cls_df.iterrows():
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if len(row["标题"]) > 1:
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cls_content += f'<tr><th scope="row">{row["标题"]}</th>' \
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f'<td>开发中...</td>'
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cls_content += '</tr>'
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return cctv_content, cls_content
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if __name__ == '__main__':
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date_obj = datetime.strptime('20231019', "%Y%m%d")
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print(News().news_cctv())
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