R 您是否需要标记您的文本以可视化来自LDA主题模型的数据？

我目前正在使用textmineR软件包在2016-2019年的新闻文章上运行LDA topicmodels。 然而，我对R很陌生，我不知道如何显示模型的结果

我想展示我的模型发现的8个主题在我收集数据期间的流行情况。数据在数据帧中结构化。我的数据在日常级别定义为%y-%m-%d

我的LDA模型是这样制作的：

## get textmineR dtm
dtm <- CreateDtm(doc_vec = dat$fulltext, # character vector of documents
                 ngram_window = c(1, 2), 
                 doc_names = dat$names,
                 stopword_vec = c(stopwords::stopwords("da"), custom_stopwords),
                 lower = T, # lowercase - this is the default value
                 remove_punctuation = T, # punctuation - this is the default
                 remove_numbers = T, # numbers - this is the default
                 verbose = T,
                 cpus = 4)


dtm <- dtm[, colSums(dtm) > 3]
dtm <- dtm[, str_length(colnames(dtm)) > 3]

############################################################
## RUN & EXAMINE TOPIC MODEL
############################################################

# Draw quasi-random sample from the pc
set.seed(34838)

model <- FitLdaModel(dtm = dtm, 
                     k = 8,
                     iterations = 500,
                     burnin = 200,
                     alpha = 0.1,
                     beta = 0.05,
                     optimize_alpha = TRUE,
                     calc_likelihood = TRUE,
                     calc_coherence = TRUE,
                     calc_r2 = TRUE,
                     cpus = 4) 

# model log-likelihood
plot(model$log_likelihood, type = "l")

# topic coherence
summary(model$coherence)

hist(model$coherence, 
     col= "blue", 
     main = "Histogram of probabilistic coherence")


# top terms by topic
model$top_terms1 <- GetTopTerms(phi = model$phi, M = 10)

t(model$top_terms1)

# topic prevalence
model$prevalence <- colSums(model$theta) / sum(model$theta) * 100

# prevalence should be proportional to alpha
plot(model$prevalence, model$alpha, xlab = "prevalence", ylab = "alpha")

##获取textmineR dtm
dtm标记化发生在
CreateDtm
函数中。所以，听起来这不是你的问题

通过对作为结果模型一部分的矩阵
θ
列取平均值，可以得到一组文档中主题的流行程度

我不能用你的数据给你一个确切的答案，但我可以用
nih_样本
textmineR附带的数据给你一个类似的例子

# load the NIH sample data
data(nih_sample)

# create a dtm and topic model
dtm <- CreateDtm(doc_vec = nih_sample$ABSTRACT_TEXT, 
                 doc_names = nih_sample$APPLICATION_ID)

m <- FitLdaModel(dtm = dtm, k = 20, iterations = 100, burnin = 75)

# aggregate theta by the year of the PROJECT_END variable
end_year <- stringr::str_split(string = nih_sample$PROJECT_END, pattern = "/")

end_year <- sapply(end_year, function(x) x[length(x)])

end_year <- as.numeric(end_year)

topic_by_year <- by(data = m$theta, INDICES = end_year, FUN = function(x){
     if (is.null(nrow(x))) {
         # if only one row, gets converted to a vector
         # just return that vector
         return(x)
    } else { # if multiple rows, then aggregate
         return(colMeans(x))
     }
 })

topic_by_year <- as.data.frame(do.call(rbind, topic_by_year))

topic_by_year <- as.data.frame(do.call(rbind, topic_by_year))

# plot topic 10's prevalence by year
plot(topic_by_year$year, topic_by_year$t_10, type = "l")

#加载NIH样本数据
数据（nih_样本）
#创建dtm和主题模型
数字地面模型