Thematic clustering of text Please advise on starting points, research (papers,frameworks) related to thematic clustering of text. In  particular on a system with two levels of clustering where second level has a temporal nature.
Thanks!

Update:

Sorry for ambiguity in my initial question. I need to clarify, that I have experience with clustering in general and document clustering in particular, for text using TFIDF, word embeddings (word2vec, Glove and BERT Sentence embeddings) in a vector space. 

My question originates from some text mentioning "two levels of thematic clustering of text where second level has a temporal nature". Just wanted to know about this technique and in particular "temporal nature" used in clustering.
Your question is way to vague. What did you try, what do you want to achieve in detail. However, find below some R examples how to approach topic modeling. This book might also be helpful: Text Mining with R.

library(topicmodels)
data("AssociatedPress")
AssociatedPress

# LDA (Latent Dirichlet Allocation)
ap_lda &lt;- LDA(AssociatedPress, k = 2, control = list(seed = 1234))
ap_lda

# Get "topics"
library(tidytext)
ap_topics &lt;- tidy(ap_lda, matrix = "beta")
ap_topics

library(ggplot2)
library(dplyr)

# Plot topics
ap_top_terms &lt;- ap_topics %&gt;%
  group_by(topic) %&gt;%
  top_n(10, beta) %&gt;%
  ungroup() %&gt;%
  arrange(topic, -beta)

ap_top_terms %&gt;%
  mutate(term = reorder(term, beta)) %&gt;%
  ggplot(aes(term, beta, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip()

# (words with) Greatest difference between categories
library(tidyr)

beta_spread &lt;- ap_topics %&gt;%
  mutate(topic = paste0("topic", topic)) %&gt;%
  spread(topic, beta) %&gt;%
  filter(topic1 &gt; .001 | topic2 &gt; .001) %&gt;%
  mutate(log_ratio = log2(topic2 / topic1))

beta_spread

list(beta_spread$term)

# Select 10 largest and smallest values of "log_ratio")
df1 &lt;- subset(beta_spread, beta_spread$log_ratio&lt;=max(tail(sort(beta_spread$log_ratio, decreasing = T),10)))
df2 &lt;- subset(beta_spread, beta_spread$log_ratio&gt;=min(tail(sort(beta_spread$log_ratio, decreasing = F),10)))
df = rbind(df1,df2)
df

# Plot
barplot(sort(df$log_ratio), names.arg=df$term[order(df$log_ratio)], las=2)

# Gamma contains the probability of document X to belong to topic Y
ap_documents &lt;- tidy(ap_lda, matrix = "gamma")
ap_documents
# the model estimates that only about 24.8% of the words in document 1 were generated from topic 1