【Python数据分析学习实例】动态新闻标题热点挖掘

问题描述

新闻标题是新闻的主旨，从新闻标题中可以进行多种内容的挖掘，通过对学校新闻网的新闻进行分析获得热点词，基于这些标题中的热点词设计并实现构建词云的算法，最后根据词云模型统计热点词的词频，可以快速了解这段时间学校的工作重心和工作内容。

代码实现

实现思路

1.导入所需要的包

import requestsimport jiebaimport wordcloudimport matplotlib.pyplot as pltfrom os import pathfrom lxml import etree

2.伪装成浏览器用户

headers={'Host': 'www.','Referer': 'http://www./info/1034/1502.htm','User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.101 Safari/537.36 Edg/91.0.864.48' }

3.发送请求获取响应信息

def getjson(url):response = requests.get(url, headers=headers, timeout=3)if response.status_code == 200:return response.content.decode("utf-8")else:print("Fail to get page")return None

测试

response = getjson('http://www./xwdt/xxxw.htm')response

4.爬虫选择器定位

浏览器打开F12，查看页面结构，我们需要得到的是li标签中的信息

可以看出我们需要的信息在‘/html/body/div[3]/div/div[2]/div[2]/ul/li’

response = getjson(url)tree = etree.HTML(response)li_list = tree.xpath('/html/body/div[3]/div/div[2]/div[2]/ul/li')li_list

info_list = []for li in li_list :title = li.xpath('./a/text()') # 获取a标签内容url = li.xpath('./a/@href')[0] # 获取a标签href链接item = {}item['title'] = titleitem['url'] = 'http://www./' + url[3:]info_list.append(item)info_list.append(title)info_list

5.屏蔽无用词并分词

article_dict = {}content = ''.join('%s' %a for a in info_list)article_jieba = jieba.lcut(content)stop_words = set(line.strip() for line in open('stopwords.txt',encoding='utf-8'))for data in article_jieba:if len(data) == 1 or data in stop_words:continueif data not in article_dict:article_dict[data] = 1else:article_dict[data] += 1article_list = list(article_dict.items())cloud_data = ''for word in article_list:cloud_data += word[0] + ' '

完整代码

import requestsimport jiebaimport wordcloudimport matplotlib.pyplot as pltfrom os import pathfrom lxml import etree# 伪装用户浏览器headers={'Host': 'www.','Referer': 'http://www./info/1034/1502.htm','User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.101 Safari/537.36 Edg/91.0.864.48' }def getjson(url):# 发送请求，得到响应消息response = requests.get(url, headers=headers, timeout=3)# 响应信息解析if response.status_code == 200:return response.content.decode("utf-8")else:print("Fail to get page")return Nonedef getPageInfo():info_list=[]for i in range (0,157):if i == 0:url = 'http://www./xwdt/xxxw.htm'else:i = str(i)url = 'http://www./xwdt/xxxw/' + i + '.htm'response = getjson(url)tree = etree.HTML(response)# 爬虫选择器定位li_list = tree.xpath('/html/body/div[3]/div/div[2]/div[2]/ul/li')for li in li_list :# 获取新闻标题title = li.xpath('./a/text()')info_list.append(title)return info_listdef createWordCloud(info_list):article_dict = {}# 将新闻标题连接成一串字符content = ''.join('%s' %a for a in info_list)# 使用jieba分词article_jieba = jieba.lcut(content)# 加载屏蔽词stop_words = set(line.strip() for line in open('stopwords.txt',encoding='utf-8'))# 取出有用的的词for data in article_jieba:if len(data) == 1 or data in stop_words:continueif data not in article_dict:article_dict[data] = 1else:article_dict[data] += 1article_list = list(article_dict.items())cloud_data = ''for word in article_list:cloud_data += word[0] + ' 'backgroud_Image = plt.imread('bg.jpg')w = wordcloud.WordCloud(# 设置背景颜色background_color='white', # 设置背景图片mask=backgroud_Image, # 若是有中文的话，这句代码必须添加，不然会出现方框，不出现汉字font_path = "simhei.ttf", # 设置最大显示的字数max_words=2000, # 设置有多少种随机生成状态，即有多少种配色方案random_state=30, # 设置字体最大值max_font_size=200, )w.generate(cloud_data)# 显示词云图plt.imshow(w)# 是否显示x轴、y轴下标plt.axis('off')plt.show()# 获得模块所在的路径d = path.dirname(__file__)w.to_file(path.join(d, "词云.jpg"))if __name__ == '__main__':createWordCloud(getPageInfo())

实验结果

【资源下载】

使用的屏蔽词库【点击下载】

使用的字体【点击下载】