1.jieba.analyse.extract_tags(text)  text必须是一连串的字符串才可以

第一步:进行语料库的读取

第二步:进行分词操作

第三步:载入停用词,同时对分词后的语料库进行停用词的去除

第四步:选取一段文本分词列表,串接成字符串,使用jieba.analyse.extract_tags提取主题词

import pandas as pd
import numpy as np
import jieba # 1.导入数据语料的新闻数据
df_data = pd.read_table('data/val.txt', names=['category', 'theme', 'URL', 'content'], encoding='utf-8') # 2.对语料库进行分词操作
df_contents = df_data.content.values.tolist() # list of list 结构
Jie_content = []
for df_content in df_contents:
split_content = jieba.lcut(df_content)
if len(split_content) > 1 and split_content != '\t\n':
Jie_content.append(split_content) # 3. 导入停止词的语料库, sep='\t'表示分隔符, quoting控制引号的常量, names=列名, index_col=False,不用第一列做为行的列名, encoding
stopwords = pd.read_csv('stopwords.txt', sep='\t', quoting=3, names=['stopwords'], index_col=False, encoding='utf-8')
print(stopwords.head()) # 对文本进行停止词的去除
def drop_stops(Jie_content, stopwords):
clean_content = []
all_words = []
for j_content in Jie_content:
line_clean = []
for line in j_content:
if line in stopwords:
continue
line_clean.append(line)
all_words.append(line)
clean_content.append(line_clean) return clean_content, all_words
# 将DateFrame的stopwords数据转换为list形式
stopwords = stopwords.stopwords.values.tolist()
clean_content, all_words = drop_stops(Jie_content, stopwords)
print(clean_content[0]) #4. 使用jieba分词器,提取文本的关键字
import jieba.analyse
index = 2000
content_word = ''.join(clean_content[index]) content_text = ' '.join(jieba.analyse.extract_tags(content_word, topK=5, withWeight=False))
print(content_word)
print(content_text)
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