nlp - R text2vec パッケージ内 - LDA モデルによって生成されたトピックを関連ドキュメントに割り当てる方法

Question

Rで実装されたLDAモデルでtext2vecパッケージを使用していますが、各ドキュメントをトピックに割り当てる方法を考えています

BELOW HERE is my code:

library(stringr)
library(rword2vec)
library(wordVectors)
#install.packages("text2vec")
library(text2vec)
library(data.table)
library(magrittr)

prep_fun = function(x) {
  x %>% 
    # make text lower case
    str_to_lower %>% 
    # remove non-alphanumeric symbols
    str_replace_all("[^[:alpha:]]", " ") %>% 
    # collapse multiple spaces
    str_replace_all("\\s+", " ")
}
movie_review_train = prep_fun(movie_review_train)

tokens = movie_review_train[1:1000] %>% 
  tolower %>% 
  word_tokenizer
it = itoken(tokens, progressbar = FALSE)
v = create_vocabulary(it)
v
vectorizer = vocab_vectorizer(v)
t1 = Sys.time()
dtm_train = create_dtm(it, vectorizer)
print(difftime(Sys.time(), t1, units = 'sec'))
dim(dtm_train)
stop_words = c("i", "me", "my", "myself", "we", "our", "ours", "ourselves")
t1 = Sys.time()
v = create_vocabulary(it, stopwords = stop_words)
print(difftime(Sys.time(), t1, units = 'sec'))
pruned_vocab = prune_vocabulary(v, 
                                term_count_min = 10, 
                                doc_proportion_max = 0.5,
                                doc_proportion_min = 0.001)
vectorizer = vocab_vectorizer(pruned_vocab)
# create dtm_train with new pruned vocabulary vectorizer
t1 = Sys.time()
dtm_train  = create_dtm(it, vectorizer)
print(difftime(Sys.time(), t1, units = 'sec'))
dtm_train_l1_norm = normalize(dtm_train, "l1")
tfidf = TfIdf$new()
# fit model to train data and transform train data with fitted model
dtm_train_tfidf = fit_transform(dtm_train, tfidf)

dtm = transform(dtm_train_tfidf, tfidf)
lda_model <-LDA$new(n_topics = ntopics
                    ,doc_topic_prior = alphaprior
                    ,topic_word_prior = deltaprior
)
lda_model$get_top_words(n = 10, topic_number = c(1:5), lambda = 0.3)

この後、各ドキュメントを関連トピックに割り当てたいと思います。トピックの下に用語のリストを取得していますが、マッピング方法はわかりません。