August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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238
｜
第
8
章
或者，我们也可以用图形表示（将矩阵乘法所需的维度表示成图形），如图
8-1
所示。
在低维度下，分解计算可以得到精确的结果。但是高维度下所需的计算量太大，因
此近似分解就足够了。
文档
文档
单词
单词
主题
主题
图 8-1：非负矩阵分解原理图。矩阵 V 被分解成了 W 和 H
在文本分析的语境中，
W
和
H
都有自己的释义。矩阵
W
的行数与文档词条矩阵相
同，因此
W
的行映射成了主题（因此
W
是文档主题矩阵）。
H
的列数与特征数相同，
因此它展示了构成主题的特征（因此
H
是主题特征矩阵）。主题的数量（
W
的列数
与
H
的行数）可以任意选择。这个数量越小，分解的精度就越低。
8.3.1
案例：使用
NMF
创建文档的主题模型
通过
scikit-learn
，针对发言执行上述分解会非常简单。（几乎）所有的
主题模型都需要主题数量作为参数，我们随便选择
10
个主题（后面我们
就会发现这个选择很不错）：
from sklearn.decomposition import NMF
nmf_text_model = NMF(n_components=10, random_state=42)
W_text_matrix = nmf_text_model.fit_transform(tfidf_text_vectors)
H_text_matrix = nmf_text_model.components_
与
TfidfVectorizer
类似，
NMF
也有一个
fit_transform
方法，可以返回其中一个
正因子矩阵。而其他因子则可以通过
NMF
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