中文词频统计与词云生成

 本次作业的要求来自于：https://edu.cnblogs.com/campus/gzcc/GZCC-16SE1/homework/2822

初始化jieba环境：

 1. 下载一长篇中文小说。

2. 从文件读取待分析文本。

3. 安装并使用jieba进行中文分词。

pip install jieba

import jieba

jieba.lcut(text)

 

4. 更新词库，加入所分析对象的专业词汇。

jieba.add_word('天罡北斗阵')  #逐个添加

jieba.load_userdict(word_dict)  #词库文本文件

test=open(r"..python1	hreeCountry.txt", "r",encoding="utf-8").read()
File=open(r"..python1stops_chinese.txt", "r",encoding="utf-8")
jieba.load_userdict(r"..python1比较全的三国人名.txt")
#停词表
stops = File.read().split('
')
ch="《》
：，。、-!?"

转换代码：scel_to_text

# -*- coding: utf-8 -*-
import struct
import os

# 拼音表偏移，
startPy = 0x1540;

# 汉语词组表偏移
startChinese = 0x2628;

# 全局拼音表
GPy_Table = {}

# 解析结果
# 元组(词频,拼音,中文词组)的列表


# 原始字节码转为字符串
def byte2str(data):
pos = 0
str = ''
while pos < len(data):
c = chr(struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0])
if c != chr(0):
str += c
pos += 2
return str

# 获取拼音表
def getPyTable(data):
data = data[4:]
pos = 0
while pos < len(data):
index = struct.unpack('H', bytes([data[pos],data[pos + 1]]))[0]
pos += 2
lenPy = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]
pos += 2
py = byte2str(data[pos:pos + lenPy])

GPy_Table[index] = py
pos += lenPy

# 获取一个词组的拼音
def getWordPy(data):
pos = 0
ret = ''
while pos < len(data):
index = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]
ret += GPy_Table[index]
pos += 2
return ret

# 读取中文表
def getChinese(data):
GTable = []
pos = 0
while pos < len(data):
# 同音词数量
same = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

# 拼音索引表长度
pos += 2
py_table_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

# 拼音索引表
pos += 2
py = getWordPy(data[pos: pos + py_table_len])

# 中文词组
pos += py_table_len
for i in range(same):
# 中文词组长度
c_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]
# 中文词组
pos += 2
word = byte2str(data[pos: pos + c_len])
# 扩展数据长度
pos += c_len
ext_len = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]
# 词频
pos += 2
count = struct.unpack('H', bytes([data[pos], data[pos + 1]]))[0]

# 保存
GTable.append((count, py, word))

# 到下个词的偏移位置
pos += ext_len
return GTable


def scel2txt(file_name):
print('-' * 60)
with open(file_name, 'rb') as f:
data = f.read()

print("词库名：", byte2str(data[0x130:0x338])) # .encode('GB18030')
print("词库类型：", byte2str(data[0x338:0x540]))
print("描述信息：", byte2str(data[0x540:0xd40]))
print("词库示例：", byte2str(data[0xd40:startPy]))

getPyTable(data[startPy:startChinese])
getChinese(data[startChinese:])
return getChinese(data[startChinese:])

if __name__ == '__main__':
# scel所在文件夹路径
in_path = r"F:	ext" #修改为你的词库文件存放文件夹
# 输出词典所在文件夹路径
out_path = r"F:	ext" # 转换之后文件存放文件夹
fin = [fname for fname in os.listdir(in_path) if fname[-5:] == ".scel"]
for f in fin:
try:
for word in scel2txt(os.path.join(in_path, f)):
file_path=(os.path.join(out_path, str(f).split('.')[0] + '.txt'))
# 保存结果
with open(file_path,'a+',encoding='utf-8')as file:
file.write(word[2] + '
')
os.remove(os.path.join(in_path, f))
except Exception as e:
print(e)
pass

5. 生成词频统计

for w in tokens:
    if len(w)==1:
        continue
    else:
        wordict[w] = wordict.get(w,0)+1

7. 排除语法型词汇，代词、冠词、连词等停用词。

stops

tokens=[token for token in wordsls if token not in stops]

wordlist=jieba.lcut(test)
tokens=[token for token in wordlist if token not in stops]

8. 输出词频最大TOP20，把结果存放到文件里。

for i in range(20):
    print(wordsort[i])

9. 生成词云。

pd.DataFrame(wordsort).to_csv('three.csv', encoding='utf-8')
txt = open('three.csv','r',encoding='utf-8').read()
cut_text=''.join(txt)
from wordcloud import WordCloud
mywc=WordCloud().generate(cut_text)
import matplotlib.pyplot as plt
plt.imshow(mywc)
plt.axis("off")
plt.show()
mywc.to_file(r"F:	xt	hreestory.png")

整体代码：

# _*_ coding: utf-8 _*_
import jieba
import pandas as pd

test=open(r"..python1	hreeCountry.txt", "r",encoding="utf-8").read()
File=open(r"..python1stops_chinese.txt", "r",encoding="utf-8")
jieba.load_userdict(r"..python1比较全的三国人名.txt")
#停词表
stops = File.read().split('
')
ch="《》
：，。、-!?"
for c in ch:
    test = test.replace(c,'')

    #更新词库，加入所分析对象的专业词汇
jieba.add_word('哎哟不错哟')
#中文切词
wordlist=jieba.lcut(test)
tokens=[token for token in wordlist if token not in stops]
wordict={}
for w in tokens:
    if len(w)==1:
        continue
    else:
        wordict[w] = wordict.get(w,0)+1
wordsort=list(wordict.items())
wordsort.sort(key= lambda x:x[1],reverse=True)
#输出词频最大TOP20
for i in range(20):
    print(wordsort[i])

pd.DataFrame(wordsort).to_csv('three.csv', encoding='utf-8')
txt = open('three.csv','r',encoding='utf-8').read()
cut_text=''.join(txt)
from wordcloud import WordCloud
mywc=WordCloud().generate(cut_text)
import matplotlib.pyplot as plt
plt.imshow(mywc)
plt.axis("off")
plt.show()
mywc.to_file(r"F:	xt	hreestory.png")