原文：https://blog.csdn.net/selinda001/article/details/80446766
原文代码需要一点点调整才能跑通
from nltk.stem.wordnet import WordNetLemmatizer import string import pandas as pd import gensim from gensim import corpora 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] #加载停用词 stopwords=pd.read_csv('stopwords.txt',index_col=False,quoting=3,sep="\t",names=['stopword'], encoding='utf-8') stopwords=stopwords['stopword'].values exclude = set(string.punctuation) lemma = WordNetLemmatizer() def clean(doc): stop_free = " ".join([i for i in doc.lower().split() if i not in stopwords]) 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 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))
