导包

import nltk, urllib.request
from bs4 import BeautifulSoup
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import jieba
import PIL.Image as image
import numpy as np

获取网络文本，清除干扰词

response = urllib.request.urlopen('http://python.org/')
html = response.read()
html = nltk.clean_html(html)
html = BeautifulSoup(html).get_text()
tokens = [tok for tok in html.split()]

获取词频

Freq_dist_nltk=nltk.FreqDist(tokens)
print(Freq_dist_nltk.plot(50, cumulative=False))
for k,v in Freq_dist_nltk.items():
                  print(str(k)+':'+str(v)

去除停用词，画词频曲线图

stopwords = [word.strip().lower() for word in open("stop.txt")]
clean_tokens = [tok for tok in tokens if len(tok.lower()) > 1 and (tok.lower() not in stopwords)]
Freq_dist_nltk=nltk.FreqDist(clean_tokens)
print(Freq_dist_nltk.plot(50, cumulative=False))

屏幕快照 2019-04-28 下午21.14.15 下午.png

中文分词

def trans_CN(text):
      word_list = jieba.cut(text)
      result = " ".join(word_list)
      return result

生成中文词云

wordcloud = WordCloud(
         # 添加遮罩层
         mask=mask,
         # 生成中文字的字体,必须要加,不然看不到中文
         font_path="STFANGSO.ttf"
     ).generate(text)
image_produce = wordcloud.to_image()
image_produce.show()

生成英文词云

wordcloud = WordCloud(
        background_color="white", #设置背景为白色，默认为黑色
        width=1500,              #设置图片的宽度
        height=960,              #设置图片的高度
        margin=10               #设置图片的边缘
        ).generate(str(clean_tokens))
# 绘制图片
plt.imshow(wordcloud)
# 消除坐标轴
plt.axis("off")
# 展示图片
plt.show()
# 保存图片
wordcloud.to_file('my_test2.png')