中文词频统计与词云生成

本次作业来源于：https://edu.cnblogs.com/campus/gzcc/GZCC-16SE1/homework/2822

中文词频统计

1. 下载一篇长篇中文小说----《西游记》。

2. 从文件读取待分析文本。

xiyouji = open('xiyouji.txt','r',encoding='utf-8').read()

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

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:xiaoshuo"  # 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:xiaoshuo"  # 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

wcdict = {}
for word in tokens:
    if len(word)==1:
        continue
    else:
        wcdict[word] = wcdict.get(word,0)+1

6. 排序

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

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

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]

8. 输出词频最大TOP20，把结果存放到文件里

import jieba
xiyouji = open('xiyouji.txt','r',encoding='utf-8').read()
tichu = open('stops_chinese.txt','r',encoding='utf-8').read()
stops = tichu.split()

wordsls = jieba.lcut(xiyouji)
tokens = [token for token in wordsls if token not in stops]
print(len(wordsls),len(tokens))
wcdict = {}
for word in tokens:
    if len(word)==1:
        continue
    else:
        wcdict[word] = wcdict.get(word,0)+1
wcls = list(wcdict.items())
wcls.sort(key=lambda x:x[1],reverse=True)
for i in range(20):
    print(wcls[i])

 

9. 生成词云。

from wordcloud import  WordCloud
import matplotlib.pyplot as plt

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()