鬼吹灯文本挖掘3：关键词提取extract_tags和使用sklearn TfidfTransformer 计算TF-IDF矩阵 鬼吹灯文本挖掘3：关键词提取和使用sklearn 计算TF-IDF矩阵

鬼吹灯文本挖掘1：jieba分词和CountVectorizer向量化

鬼吹灯文本挖掘2：wordcloud 词云展示

鬼吹灯文本挖掘3：关键词提取和使用sklearn 计算TF-IDF矩阵

1.  jieba模块进行关键词提取 ： 数据gcd1_chap的准备参考鬼吹灯文本挖掘1

    jiaba.analyse.extract_tags() 可以使用默认的TF-IDF模型对文档进行分析。

    参数withWeight设置为True时可以显示词的权重，topK设置显示的词的个数。

# 注意：函数是在使用默认的TF-IDF模型进行分析
jieba.analyse.extract_tags(gcd1_chap.txt[1])

['胡国华','老鼠','王二','杠子','舅舅','福寿','纸人','床上','保安队','媳妇','烟瘾','军阀','大烟','典当','外甥','银元','欺负','回家',
 '一看','祖父']

jieba.analyse.extract_tags(gcd1_chap.txt[3], withWeight = True, topK=10)   # 要求返回权重值

[('燕子', 0.11287212259623317),
 ('人熊', 0.08217464601938411),
 ('胖子', 0.08007877727840527),
 ('栗子', 0.05255360038559252),
 ('田晓萌', 0.051731342654467966),
 ('我们', 0.04289266606586197),
 ('猎枪', 0.030355338860093485),
 ('大树', 0.027296309722155618),
 ('喇嘛', 0.02617937062254605),
 ('树上', 0.025039129533637614)]

2. Sklearn计算词频矩阵

     CountVectorizer可以将文本列表转换为词频矩阵sparse matrix，且为稀疏矩阵，其中参数min_df = 5 指定筛选出至少在5篇文档中出现过的词。       words_count_mat.todense() 可将稀疏矩阵转换为标准矩阵。

from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer, TfidfVectorizer
c_vectorizer = CountVectorizer(min_df = 5)
words_count_mat = c_vectorizer.fit_transform(gcd1_words_list)      # 将文本列表转换为词频矩阵
words_count_mat

<33x1654 sparse matrix of type '<class 'numpy.int64'>'
	with 16484 stored elements in Compressed Sparse Row format>

words_count_mat.todense()

matrix([[ 0,  0,  4, ...,  0,  0,  2],
        [ 0,  0,  2, ...,  0,  0,  1],
        [ 3,  0, 16, ...,  0,  0,  1],
        ...,
        [ 3,  0, 13, ...,  0,  0,  1],
        [ 1,  0,  4, ...,  0,  8,  1],
        [ 0,  0,  5, ...,  0,  0,  0]], dtype=int64)

3. Sklearn 计算 TF-IDF 矩阵

   (1) 使用TfidfTransformer:  可以将词频矩阵转换为TF-IDF矩阵

tfidf_vectorizer = TfidfTransformer()
tfidf_mat = tfidf_vectorizer.fit_transform(words_count_mat)      # 将词频矩阵转换为TF-IDF矩阵
tfidf_mat
<33x1654 sparse matrix of type '<class 'numpy.float64'>'
	with 16484 stored elements in Compressed Sparse Row format>

tfidf_mat.todense()

matrix([[0.        , 0.        , 0.09026236, ..., 0.        , 0.        ,
         0.08517622],
        [0.        , 0.        , 0.03498056, ..., 0.        , 0.        ,
         0.03300946],
        [0.0211808 , 0.        , 0.08639816, ..., 0.        , 0.        ,
         0.01019122],
        ...,
        [0.03808981, 0.        , 0.1262392 , ..., 0.        , 0.        ,
         0.01832705],
        [0.01730247, 0.        , 0.0529336 , ..., 0.        , 0.22533467,
         0.02497544],
        [0.        , 0.        , 0.04403064, ..., 0.        , 0.        ,
         0.        ]])

（2）使用TfidfVectorizer: 可以将文本列表直接转换为TF-IDF矩阵，相当于CountVectorizer + TfidfTransformer的效果

tfidf_vectorizer = TfidfVectorizer(min_df=5)    CountVectorizer + TfidfTransformer
tfidf_mat2 = tfidf_vectorizer.fit_transform(gcd1_words_list)
tfidf_mat2

<33x1654 sparse matrix of type '<class 'numpy.float64'>'
	with 16484 stored elements in Compressed Sparse Row format>

tfidf_mat2.todense()

matrix([[0.        , 0.        , 0.09026236, ..., 0.        , 0.        ,
         0.08517622],
        [0.        , 0.        , 0.03498056, ..., 0.        , 0.        ,
         0.03300946],
        [0.0211808 , 0.        , 0.08639816, ..., 0.        , 0.        ,
         0.01019122],
        ...,
        [0.03808981, 0.        , 0.1262392 , ..., 0.        , 0.        ,
         0.01832705],
        [0.01730247, 0.        , 0.0529336 , ..., 0.        , 0.22533467,
         0.02497544],
        [0.        , 0.        , 0.04403064, ..., 0.        , 0.        ,
         0.        ]])