关键字提取-TF-IDF算法和TextRank算法

关键字提取-TF-IDF算法和TextRank算法

import pandas as pd
raw = pd.read_table('../data/金庸-射雕英雄传txt精校版.txt', names=['txt'], encoding="GBK")

#  章节判断用变量预处理
def m_head(tmpstr):
    return tmpstr[:1]
 
def m_mid(tmpstr):
    return tmpstr.find("回 ")
 
raw['head'] = raw.txt.apply(m_head)
raw['mid'] = raw.txt.apply(m_mid)
raw['len'] = raw.txt.apply(len)


#  章节判断
chapnum = 0
for i in range(len(raw)):
    if raw['head'][i] == "第" and raw['mid'][i] > 0 and raw['len'][i] < 30:
        chapnum += 1
    if chapnum >= 40 and raw['txt'][i] == "附录一：成吉思汗家族":
        chapnum = 0
    raw.loc[i, 'chap'] = chapnum
                             

#  删除临时变量
del raw['head']
del raw['mid']
del raw['len']


rawgrp = raw.groupby('chap')
chapter = rawgrp.agg(sum)  # 只有字符串的情况下，sum函数自动转为合并字符串
chapter = chapter[chapter.index != 0]
chapter

import jieba
import jieba.analyse
#  注意：函数在使用默认的TFIDF模型进行分析
jieba.analyse.extract_tags(chapter.txt[1])

#  要求返回权重值
jieba.analyse.extract_tags(chapter.txt[1], withWeight=True)

#  应用自定义词典改善分词效果
jieba.load_userdict('../data/金庸小说词库.txt')  #  dict为自定义词典的路径

#  在TFIDF计算中直接应用停用词表
jieba.analyse.set_stop_words('../data/停用词.txt')

TFres = jieba.analyse.extract_tags(chapter.txt[1], withWeight=True)
TFres

#  使用自定义TF-IDF频率文件
jieba.analyse.set_idf_path('../data/idf.txt.big')

TFres = jieba.analyse.extract_tags(chapter.txt[1], withWeight=True)
TFres

from sklearn.feature_extraction.text import TfidfTransformer

txtlist = [" ".join(m_cut(w)) for w in chapter.txt.iloc[:5]]

vectorizer = CountVectorizer()
x = vectorizer.fit_transform(txtlist)  # 将文本中的词语转换成词频矩阵

transformer = TfidfTransformer()
tfidf = transformer.fit_transform(x)  #  基于词频矩阵x计算TF-IDF
tfidf

#  转换成数组
tfidf.toarray()

#  转换成矩阵
tfidf.todense()

tfidf.todense().shape

print("字典长度：", len(vectorizer.vocabulary_))
vectorizer.vocabulary_

#  文档分词及预处理
chaplist = [m_cut(w) for w in chapter.txt.iloc[:5]]
chaplist