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手记实用系列文章：
1结巴分词和自然语言处理HanLP处理手记
代码封装类：
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import jieba
import os
import re
import time
from jpype import *
'''
title：利用结巴分词进行文本语料的批量处理
1 首先对文本进行遍历查找
2 创建原始文本的保存结构
3 对原文本进行结巴分词和停用词处理
4 对预处理结果进行标准化格式，并保存原文件结构路径
author：白宁超
myblog：http://www.cnblogs.com/baiboy/
time：2017年4月28日10:03:09
'''
'''
创建文件目录
path:根目录下创建子目录
'''
def mkdir(path):
# 判断路径是否存在
isExists=os.path.exists(path)
# 判断结果
if not isExists:
os.makedirs(path)
print(path+' 创建成功')
return True
else:
pass
print('-->请稍后，文本正在预处理中...')
'''
结巴分词工具进行中文分词处理：
read_folder_path：待处理的原始语料根路径
write_folder_path 中文分词经数据清洗后的语料
'''
def CHSegment(read_folder_path,write_folder_path):
stopwords ={}.fromkeys([line.strip() for line in open('../Database/stopwords/CH_stopWords.txt','r',encoding='utf-8')]) # 停用词表
# 获取待处理根目录下的所有类别
folder_list = os.listdir(read_folder_path)
# 类间循环
# print(folder_list)
for folder in folder_list:
#某类下的路径
new_folder_path = os.path.join(read_folder_path, folder)
# 创建一致的保存文件路径
mkdir(write_folder_path+folder)
#某类下的保存路径
save_folder_path = os.path.join(write_folder_path, folder)
#某类下的全部文件集
# 类内循环
files = os.listdir(new_folder_path)
j = 1
for file in files:
if j > len(files):
break
# 读取原始语料
raw = open(os.path.join(new_folder_path, file),'r',encoding='utf-8').read()
# 只保留汉字
# raw1 = re.sub("[A-Za-z0-9\[\`\~\!\@\#\$\^\&\*\(\)\=\|\{\}\'\:\;\'\,\[\]\.\<\>\/\?\~\！\@\#\\\&\*\%]", "", raw)
# jieba分词
wordslist = jieba.cut(raw, cut_all=False) # 精确模式
# 停用词处理
cutwordlist=''
for word in wordslist:
if word not in stopwords and word=="\n":
cutwordlist+="\n" # 保持原有文本换行格式
elif len(word)>1 :
cutwordlist+=word+"/" #去除空格
#保存清洗后的数据
with open(os.path.join(save_folder_path,file),'w',encoding='utf-8') as f:
f.write(cutwordlist)
j += 1
'''
结巴分词工具进行中文分词处理：
read_folder_path：待处理的原始语料根路径
write_folder_path 中文分词经数据清洗后的语料
'''
def HanLPSeg(read_folder_path,write_folder_path):
startJVM(getDefaultJVMPath(), "-Djava.class.path=C:\hanlp\hanlp-1.3.2.jar;C:\hanlp", "-Xms1g", "-Xmx1g") # 启动JVM，Linux需替换分号;为冒号:
stopwords ={}.fromkeys([line.strip() for line in open('../Database/stopwords/CH_stopWords.txt','r',encoding='utf-8')]) # 停用词表
# 获取待处理根目录下的所有类别
folder_list = os.listdir(read_folder_path)
# 类间循环
# print(folder_list)
for folder in folder_list:
#某类下的路径
new_folder_path = os.path.join(read_folder_path, folder)
# 创建一致的保存文件路径
mkdir(write_folder_path+folder)
#某类下的保存路径
save_folder_path = os.path.join(write_folder_path, folder)
#某类下的全部文件集
# 类内循环
files = os.listdir(new_folder_path)
j = 1
for file in files:
if j > len(files):
break
# 读取原始语料
raw = open(os.path.join(new_folder_path, file),'r',encoding='utf-8').read()
# HanLP分词
HanLP = JClass('com.hankcs.hanlp.HanLP')
wordslist = HanLP.segment(raw)
#保存清洗后的数据
wordslist1=str(wordslist).split(",")
# print(wordslist1[1:len(wordslist1)-1])
flagresult=""
# 去除标签
for v in wordslist1[1:len(wordslist1)-1]:
if "/" in v:
slope=v.index("/")
letter=v[1:slope]
if len(letter)>0 and '\n\u3000\u3000' in letter:
flagresult+="\n"
else:flagresult+=letter +"/" #去除空格
# print(flagresult)
with open(os.path.join(save_folder_path,file),'w',encoding='utf-8') as f:
f.write(flagresult.replace(' /',''))
j += 1
shutdownJVM()
if __name__ == '__main__' :
print('开始进行文本分词操作：\n')
t1 = time.time()
dealpath="../Database/SogouC/FileTest/"
savepath="../Database/SogouCCut/FileTest/"
# 待分词的语料类别集根目录
read_folder_path = '../Database/SogouC/FileNews/'
write_folder_path = '../Database/SogouCCut/'
#jieba中文分词
CHSegment(read_folder_path,write_folder_path) #300个txtq其中结巴分词使用3.31秒
HanLPSeg(read_folder_path,write_folder_path) #300个txt其中hanlp分词使用1.83秒
t2 = time.time()
print('完成中文文本切分: '+str(t2-t1)+"秒。")
运行效果：
文章来源于白宁超的博客
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