Question

我正在尝试使用Gensim python库学习主题建模。我尝试了很多不同的教程，包括官方教程。

问题：如何使用Gensim获得文档明智的主题分发。

我当前的输出是主题列表及其关键字和概率，如下所示。

(0, u'0.086*good + 0.086*brocolli + 0.086*health + 0.061*eat)
(1, u'0.068*mother + 0.068*brother + 0.068*drive + 0.041*pressur)

我想知道是否可以列出说明该特定文档的每个文档和热门主题的列表？

我的代码如下：

tokenizer = RegexpTokenizer(r'\w+')

# create English stop words list
en_stop = get_stop_words('en')

# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()

# create sample documents
doc_a = "Brocolli is good to eat. My brother likes to eat good brocolli, but not my mother."
doc_b = "My mother spends a lot of time driving my brother around to baseball practice."
doc_c = "Some health experts suggest that driving may cause increased tension and blood pressure."
doc_d = "I often feel pressure to perform well at school, but my mother never seems to drive my brother to do better."
doc_e = "Health professionals say that brocolli is good for your health." 

# compile sample documents into a list
doc_set = [doc_a, doc_b, doc_c, doc_d, doc_e]
num_topics=2;

# list for tokenized documents in loop
texts = []

# loop through document list
for i in doc_set:

    # clean and tokenize document string
    raw = i.lower()
    tokens = tokenizer.tokenize(raw)

    # remove stop words from tokens
    stopped_tokens = [i for i in tokens if not i in en_stop]
    #print(stopped_tokens)

    # stem tokens
    stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
    #print("printing stemmed tokens")
    #print(stemmed_tokens)

    # add tokens to list
    texts.append(stemmed_tokens)

# turn our tokenized documents into a id <-> term dictionary
dictionary = corpora.Dictionary(texts)
print("printing each token/words along with unique integer id..")
print(dictionary.token2id)

# convert tokenized documents into a document-term matrix
corpus = [dictionary.doc2bow(text) for text in texts]
print("printing sample bag of words")
print(corpus[0])

# generate LDA model
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=2, id2word = dictionary, passes=20)
#print(ldamodel.print_topics(num_topics=3, num_words=3))
#print ldamodel.top_topics(corpus,2)
print(ldamodel.show_topics(num_topics=2, num_words=3, log=False, formatted=True))
print(ldamodel.show_topics())
print("from for loop.")
for i in ldamodel.show_topics(len(dictionary)):
    print i

Answer 1

for i in range(len(corpus)):
    print 'doc id:'+str(i)
    print (ldamodel[corpus[i]])
    i=i+1

如果要保存主题分发，请使用以下

topicsDist=[]
for x in corpus:
    topics = ldamodel[x]
    temp=[0]* num_topics
    for t in topics:
        temp[t[0]]=t[1]
    topicsDist.append(temp)
print("topics generated")

第二个代码基本上会为语料库中的每个doc创建一个vectors = num_topics，并填充特定主题编号索引中的概率。

在Gensim

1 个答案: