python聚类分析提取名词
时间: 2023-07-04 13:27:22 浏览: 61
可以使用Python中的自然语言处理工具NLTK(Natural Language Toolkit)来实现这个功能。具体步骤如下:
1. 导入必要的库和数据
```python
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
# 示例文本数据
text = "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data."
```
2. 对文本进行分词、去停用词、词形还原等预处理操作
```python
# 分词
tokens = word_tokenize(text)
# 去除停用词
stop_words = set(stopwords.words('english'))
filtered_tokens = [token for token in tokens if token.lower() not in stop_words]
# 词形还原
lemmatizer = WordNetLemmatizer()
lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens]
```
3. 对处理后的文本进行TF-IDF向量化
```python
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform([" ".join(lemmatized_tokens)])
```
4. 使用K-Means算法进行聚类
```python
# 定义聚类数量
k = 2
# 进行K-Means聚类
kmeans = KMeans(n_clusters=k, random_state=0)
kmeans.fit(tfidf)
# 获取各簇中心
centers = kmeans.cluster_centers_
# 获取各簇中的关键词
indices = centers.argsort()[:, ::-1]
features = vectorizer.get_feature_names()
cluster_keywords = []
for i in range(k):
cluster_keywords.append([features[ind] for ind in indices[i, :5]])
```
这样,就可以得到分簇后每个簇中的关键词了。