Python是一門很強(qiáng)大的編程語言，在文本挖掘方面也有很多的庫可以使用，下面介紹一些常用的Python文本挖掘庫：

import re
#正則表達(dá)式，用于處理文字
text = "Hello, my name is John. I live in New York."
pattern = r'\w+'
result = re.findall(pattern, text)
print(result)
#輸出：['Hello', 'my', 'name', 'is', 'John', 'I', 'live', 'in', 'New', 'York']
import nltk
#自然語言處理工具包，用于分析語言
text = "This is an example sentence."
tokens = nltk.word_tokenize(text)
print(tokens)
#輸出：['This', 'is', 'an', 'example', 'sentence', '.']
from sklearn.feature_extraction.text import CountVectorizer
#文本特征提取器，用于將文本轉(zhuǎn)化為向量
corpus = ['This is the first document.', 'This is the second document.', 'And this is the third document.']
cv = CountVectorizer()
X = cv.fit_transform(corpus)
print(X.toarray())
#輸出：[[1 1 1 0 0 0 1 0 1] [1 1 0 1 0 0 1 0 1] [0 1 0 0 1 1 1 1 1]]
import gensim
#用于生成文本向量
doc = ['This is the first document', 'This is the second document', 'This is the third document']
tokenized_doc = []
for d in doc:
tokenized_doc.append(d.lower().split())
model = gensim.models.Word2Vec(tokenized_doc, size=300, window=5, min_count=1, workers=4)
print(model['document'])
#輸出：array([...], dtype=float32)