python LTP对文本文件进行分词处理，加载停用词表去除停用词，按词频排序并标注词性，将结果输出为excel表格

以下是使用Python LTP库对文本文件进行分词处理，加载停用词表去除停用词，按词频排序并标注词性，并将结果输出为Excel表格的示例代码：

import os
import xlwt
from pyltp import SentenceSplitter, Segmentor, Postagger

# 设置LTP库的模型文件路径
LTP_DATA_DIR = 'ltp_data_v3.4.0'
cws_model_path = os.path.join(LTP_DATA_DIR, 'cws.model')
pos_model_path = os.path.join(LTP_DATA_DIR, 'pos.model')

# 加载停用词表
stopwords = []
with open('stopwords.txt', 'r', encoding='utf-8') as f:
    for line in f:
        stopwords.append(line.strip())

# 初始化LTP分词器和词性标注器
segmentor = Segmentor()
segmentor.load(cws_model_path)
postagger = Postagger()
postagger.load(pos_model_path)

# 读取文本文件
with open('input.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# 使用LTP分句器对文本进行分句
sentences = SentenceSplitter.split(text)

# 建立词频统计字典
word_freq_dict = {}

# 处理每个句子
for sentence in sentences:
    # 使用LTP分词器对句子进行分词
    words = segmentor.segment(sentence)
    # 使用LTP词性标注器对分词结果进行词性标注
    postags = postagger.postag(words)
    # 处理每个词语
    for word, pos in zip(words, postags):
        # 去除停用词
        if word not in stopwords:
            # 统计词频
            if word in word_freq_dict:
                word_freq_dict[word] += 1
            else:
                word_freq_dict[word] = 1

# 按词频排序
sorted_word_freq = sorted(word_freq_dict.items(), key=lambda x: x[1], reverse=True)

# 将结果输出为Excel表格
workbook = xlwt.Workbook(encoding='utf-8')
worksheet = workbook.add_sheet('Sheet1')

# 写入表头
worksheet.write(0, 0, '词语')
worksheet.write(0, 1, '词性')
worksheet.write(0, 2, '词频')

# 写入词语、词性和词频
for i in range(len(sorted_word_freq)):
    word = sorted_word_freq[i][0]
    freq = sorted_word_freq[i][1]
    pos = postagger.postag([word])[0]
    worksheet.write(i+1, 0, word)
    worksheet.write(i+1, 1, pos)
    worksheet.write(i+1, 2, freq)

# 保存Excel文件
workbook.save('output.xls')

其中，`input.txt`是待处理的文本文件，`stopwords.txt`是停用词表文件，程序将处理结果输出为`output.xls`文件。需要注意的是，由于LTP库是基于Python2开发的，因此在Python3中需要对部分代码进行修改，例如在`open()`函数中需要加上`encoding='utf-8'`参数。