版本：
新增了一个jieba库和wordcloud文件生成词云
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
__author__ = '王益夫'
__mtime__ = '2019/12/20'
'''
'''
版本修改：
V 1.0:get代码获取的文本内容，通过jieba库和词云进行分析
'''
import jieba
from wordcloud import WordCloud
from os import path
import re
import matplotlib.pyplot as plt
#from scipy.misc import imread
import imageio
file_path = path.dirname(__file__) + r'/temp'
file_name1 = r'新闻联播.txt'
file_name2 = r'StopWords.txt'
file_name3 = r'AddWords.txt'
TextPath = file_path + '/' + file_name1
StopWordsPath = file_path + '/' + file_name2
AddWordsPath = file_path + '/' + file_name3
print(AddWordsPath)
def jiebaclearText(text):
mywordslist = []
seg_list = jieba.cut(text, cut_all=False)
#seg_list = jieba.cut(TestStr, cut_all=True) 全模式：该模式将语料中所有可以组合成词的词语都构建出来，其优点是速度非常快，缺点是不能解决歧义问题，并且分词结果不太准确。
#seg_list = jieba.cut(TestStr, cut_all=False) 默认模式:该模式利用其算法将句子最精确地分隔开，适合文本分析，通常采用这种模式进行中文分词。
#seg_list = jieba.cut_for_search(TestStr) 搜索引擎模式:该模式是在精确模式基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。
liststr = "/".join(seg_list)
f_stop = open(StopWordsPath, encoding='utf-8', errors='ignore')
try:
f_stop_text = f_stop.read()
finally:
f_stop.close()
f_stop_seg_list = f_stop_text.split('
')
for myword in liststr.split('/'):
if not (myword.strip() in f_stop_seg_list) and len(myword.strip()) > 1:
mywordslist.append(myword)
return ' '.join(mywordslist)
def addWordsRulls(text):
addwords_list = set()
try:
results = re.findall('《[^》]+》', text)
for result in results:
addwords_list.add(result)
#jieba.add_word(result)
return True
except Exception as e:
raise e
addwords_list.add('EOR:ADD正则解析失败，未获取关键词！')
return False
finally:
with open(AddWordsPath, 'a+', encoding='utf-8', errors='ignore') as file_add:
for line in list(addwords_list):
file_add.write(line + '
')
def StopWordsRulls(text):
Stopwords_list = set()
try:
results = re.findall('d{4}年d{1,2}月d{1,2}日', text)
for result in results:
print(result)
Stopwords_list.add(result)
#jieba.add_word(result)
return True
except Exception as e:
raise e
Stopwords_list.add('EOR:Stop正则解析失败，未获取关键词！')
return False
finally:
with open(StopWordsPath, 'a+', encoding='utf-8', errors='ignore') as file_Stop:
for line in list(Stopwords_list):
file_Stop.write(line + '
')
def main():
with open(TextPath, encoding='utf-8', errors='ignore') as file_Text:
text = file_Text.read()
# for key in analyse.extract_tags(text, 50, withWeight=False):
# print(key)
if addWordsRulls(text) and StopWordsRulls(text):
with open(AddWordsPath, 'r', encoding='utf-8', errors='ignore') as file_read:
context = set(file_read.read())
for line in context:
jieba.add_word(line)
text_text = jiebaclearText(text)
color_mask = imageio.imread(file_path + "/template.jpeg")
cloud = WordCloud(
# 设置字体，不指定就会出现乱码
font_path="./temp/HYQiHei-25J.ttf",
# font_path=path.join(d,'simsun.ttc'),
# 设置背景色
background_color='white',
# 词云形状
mask=color_mask,
# 允许最大词汇
max_words=200,
# 最大号字体
max_font_size=40
)
# wordcloud = WordCloud(background_color="white", width=1000, height=860, margin=2).generate(text_text)
word_cloud = cloud.generate(text_text) # 产生词云
word_cloud.to_file("test.jpg") # 保存图片
# 显示词云图片
plt.imshow(word_cloud)
plt.axis('off')
plt.show()
if __name__ == '__main__':
main()