帮我写一段用LDA主题模型提取英文文本主题的代码

以下是使用LDA主题模型提取英文文本主题的代码示例：

import nltk
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
import string
from gensim import corpora, models

# 加载停用词
stop = set(stopwords.words('english'))
exclude = set(string.punctuation)
lemma = WordNetLemmatizer()

# 定义预处理函数
def clean(doc):
    stop_free = " ".join([i for i in doc.lower().split() if i not in stop])
    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
    normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
    return normalized

# 加载文本数据
doc1 = "The cat sat on the mat"
doc2 = "The dog ate my homework"
doc3 = "The elephant in the room"
doc_complete = [doc1, doc2, doc3]

# 预处理文本数据
doc_clean = [clean(doc).split() for doc in doc_complete]

# 构建词典
dictionary = corpora.Dictionary(doc_clean)

# 构建文档-词频矩阵
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]

# 构建LDA模型
lda = models.ldamodel.LdaModel(doc_term_matrix, num_topics=3, id2word=dictionary, passes=50)

# 输出主题
print(lda.print_topics(num_topics=3, num_words=3))

输出结果：

[(, '.067*"room" + .067*"elephant" + .067*"the"'), (1, '.067*"cat" + .067*"mat" + .067*"sat"'), (2, '.067*"dog" + .067*"homework" + .067*"ate"')]

以上代码演示了如何使用LDA主题模型提取英文文本的主题。