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text2vec 包中的 LDA 主题建模非常棒。确实比topicmodel快很多

但是，我不知道如何获取每个文档属于每个主题的概率，如下例所示：

    V1  V2  V3  V4
1   0.001025237 7.89E-05    7.89E-05    7.89E-05
2   0.002906977 0.002906977 0.014534884 0.002906977
3   0.003164557 0.003164557 0.003164557 0.003164557
4   7.21E-05    7.21E-05    0.000360334 7.21E-05
5   0.000804433 8.94E-05    8.94E-05    8.94E-05
6   5.63E-05    5.63E-05    5.63E-05    5.63E-05
7   0.001984127 0.001984127 0.001984127 0.001984127
8   0.003515625 0.000390625 0.000390625 0.000390625
9   0.000748503 0.000748503 0.003742515 0.003742515
10  0.000141723 0.00297619  0.000141723 0.000708617

这是 text2vec lda 的代码

ss2 <- as.character(stressor5$weibo)
seg2 <- mmseg4j(ss2)


# Create vocabulary. Terms will be unigrams (simple words).
it_test = itoken(seg2, progressbar = FALSE)
vocab2 <- create_vocabulary(it_test)


pruned_vocab2 = prune_vocabulary(vocab2, 
                                 term_count_min = 10, 
                                 doc_proportion_max = 0.5,
                                 doc_proportion_min = 0.001)


vectorizer2 <- vocab_vectorizer(pruned_vocab2)

dtm_test = create_dtm(it_test, vectorizer2)


lda_model = LDA$new(n_topics = 1000, vocabulary = vocab2, doc_topic_prior = 0.1, topic_word_prior = 0.01)

doc_topic_distr = lda_model$fit_transform(dtm_test, n_iter = 1000, convergence_tol = 0.01, check_convergence_every_n = 10)
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1 回答 1

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doc_topic_distr是一个矩阵，其中包含文档中的单词被分配给特定主题的次数。因此，您只需按字数对每一行进行规范化（也可以doc_topic_prior在规范化之前添加）。

library(text2vec)
data("movie_review")
tokens = movie_review$review %>% 
  tolower %>% 
  word_tokenizer
# turn off progressbar because it won't look nice in rmd
it = itoken(tokens, ids = movie_review$id, progressbar = FALSE)
v = create_vocabulary(it) %>% 
  prune_vocabulary(term_count_min = 10, doc_proportion_max = 0.2)
vectorizer = vocab_vectorizer(v)
dtm = create_dtm(it, vectorizer, type = "lda_c")
doc_topic_prior = 0.1
lda_model = 
  LDA$new(n_topics = 10, vocabulary = v, 
          doc_topic_prior = doc_topic_prior, topic_word_prior = 0.01)

doc_topic_distr = 
  lda_model$fit_transform(dtm, n_iter = 1000, convergence_tol = 0.01, 
                          check_convergence_every_n = 10)
head(doc_topic_distr)
#       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#5814_8   16   18    0   34    0   16   49    0   20    23
#2381_9    4    0    6   20    0    0    6    6    0    28
#7759_3   21   39    7    0    3   47    0   25   21    17
#3630_4   18    7   22   14   19    0   18    0    2    35
#9495_8    4    0   13   17   13   78    3    2   28    25
#8196_8    0    0    0   11    0    8    0    8    8     0
doc_topic_prob = normalize(doc_topic_distr, norm = "l1")
# or add norm first and normalize :
# doc_topic_prob = normalize(doc_topic_distr + doc_topic_prior, norm = "l1")
于 2016-11-27T11:52:16.403 回答