LDA主题模型英文完整python代码

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

stop = set(stopwords.words('english'))
exclude = set(string.punctuation)
lemma = WordNetLemmatizer()


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


doc1 = "Sugar is bad to consume. My sister likes to have sugar, but not my father."
doc2 = "My father spends a lot of time driving my sister around to dance practice."
doc3 = "Doctors suggest that driving may cause increased stress and blood pressure."
doc4 = "Sometimes I feel pressure to perform well at school, but my father never seems to drive my sister to do better."
doc5 = "Health experts say that Sugar is not good for your lifestyle."

# 整合文档数据
doc_complete = [doc1, doc2, doc3, doc4, doc5]

doc_clean = [clean(doc).split() for doc in doc_complete]

# 创建语料的词语词典，每个单独的词语都会被赋予一个索引
dictionary = corpora.Dictionary(doc_clean)

# 使用上面的词典，将转换文档列表（语料）变成 DT 矩阵
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]

# 使用 gensim 来创建 LDA 模型对象
Lda = gensim.models.ldamodel.LdaModel

# 在 DT 矩阵上运行和训练 LDA 模型
ldamodel = Lda(doc_term_matrix, num_topics=3, id2word=dictionary, passes=50)

# 输出结果
print(ldamodel.print_topics(num_topics=3, num_words=3))

参考
https://blog.csdn.net/selinda001/article/details/80446766