中文词频统计与词云生成

中文词频统计

1. 下载一长篇中文小说。

小说：鹿鼎记  作者：金庸

 

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

with open(r'C:\Users\Shinelon\Desktop\python 3.22\金庸小说.txt', 'r', encoding='utf-8') as f:
    jinyong = f.read().split('\n')
jieba.load_userdict(jinyong)
newtext = jieba.lcut(text)

　　

参考词库下载地址：https://pinyin.sogou.com/dict/

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"D:\360安全浏览器下载"   #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"D:\360安全浏览器下载\123"  # 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] + '\n')
            os.remove(os.path.join(in_path, f))
        except Exception as e:
            print(e)
            pass

　　

5. 生成词频统计

te = {};
for w in newtext:
    if len(w) == 1:
        continue
    else:
        te[w] = te.get(w, 0) + 1

　　

6. 排序

tesort = list(te.items())
tesort.sort(key=lambda x: x[1], reverse=True)

 

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]

with open(r'C:\Users\Shinelon\Desktop\python 3.22\stops_chinese.txt', 'r', encoding='utf-8') as f:
    stops = f.read().split('\n')

newtext2 = [text1 for text1 in newtext if text1 not in stops]

　　

8. 输出词频最大TOP20，把结果存放到文件里。

for i in range(0,20):
    print(tesort[i])

pd.DataFrame(tesort).to_csv('ludingji.csv', encoding='utf-8')

 

9. 生成词云。

txt = open('ludingji.csv','r',encoding='utf-8').read()
ludingjilist = jieba.lcut(txt)


wl_spl = "".join(ludingjilist)
mywc = WordCloud().generate(wl_spl)

plt.imshow(mywc)
plt.axis("off")
plt.show()

　　

10.代码

import pandas as pd
from wordcloud import  WordCloud
import jieba
import matplotlib.pyplot as plt

# 读取小说
f = open(r'C:\Users\Shinelon\Desktop\python 3.22\ludingji.txt', 'r', encoding='utf8')
text = f.read();
f.close();

# 加入所分析对象的专业词汇
with open(r'C:\Users\Shinelon\Desktop\python 3.22\金庸小说.txt', 'r', encoding='utf-8') as f:
    jinyong = f.read().split('\n')
jieba.load_userdict(jinyong)
newtext = jieba.lcut(text)

# 排除语法型词汇，代词、冠词、连词等停用词
with open(r'C:\Users\Shinelon\Desktop\python 3.22\stops_chinese.txt', 'r', encoding='utf-8') as f:
    stops = f.read().split('\n')
newtext2 = [text1 for text1 in newtext if text1 not in stops]

# 对词语进行出现次数统计
te = {};
for w in newtext:
    if len(w) == 1:
        continue
    else:
        te[w] = te.get(w, 0) + 1

# 次数排序
tesort = list(te.items())
tesort.sort(key=lambda x: x[1], reverse=True)

# 输出次数前TOP20的词语
for i in range(0,20):
    print(tesort[i])

# 存储结果
pd.DataFrame(tesort).to_csv('ludingji.csv', encoding='utf-8')

# 读取生成词云
txt = open('ludingji.csv','r',encoding='utf-8').read()
ludingjilist = jieba.lcut(txt)
wl_spl = "".join(ludingjilist)
mywc = WordCloud().generate(wl_spl)

plt.imshow(mywc)
plt.axis("off")
plt.show()