UDPipe Natural Language Processing - Topic Modelling Use Cases

library(udpipe)
ud_model <- udpipe_download_model(language = "french")

data(brussels_reviews)
comments <- subset(brussels_reviews, language %in% "fr")

ud_model <- udpipe_load_model(ud_model$file_model)
x <- udpipe_annotate(ud_model, x = comments$feedback, doc_id = comments$id)
x <- as.data.frame(x)

str(x)

## Define the identifier at which we will build a topic model
x$topic_level_id <- unique_identifier(x, fields = c("doc_id", "paragraph_id", "sentence_id"))
## Get a data.frame with 1 row per id/lemma
dtf <- subset(x, upos %in% c("NOUN"))
dtf <- document_term_frequencies(dtf, document = "topic_level_id", term = "lemma")
head(dtf)

## Create a document/term/matrix for building a topic model
dtm <- document_term_matrix(x = dtf)
## Remove words which do not occur that much
dtm_clean <- dtm_remove_lowfreq(dtm, minfreq = 5)
head(dtm_colsums(dtm_clean))

## Remove nouns which you really do not like (mostly too common nouns)
dtm_clean <- dtm_remove_terms(dtm_clean, terms = c("appartement", "appart", "eter"))
## Or keep of these nouns the top 50 based on mean term-frequency-inverse document frequency
dtm_clean <- dtm_remove_tfidf(dtm_clean, top = 50)

library(topicmodels)
m <- LDA(dtm_clean, k = 4, method = "Gibbs", 
         control = list(nstart = 5, burnin = 2000, best = TRUE, seed = 1:5))

## Build document term matrix on nouns/adjectives only
dtf <- subset(x, upos %in% c("NOUN", "ADJ") & 
                !lemma %in% c("appartement", "appart", "eter", "tres"))
dtf <- document_term_frequencies(dtf, document = "topic_level_id", term = "lemma")
dtm <- document_term_matrix(x = dtf)
dtm_clean <- dtm_remove_lowfreq(dtm, minfreq = 5)
## Build topic model + get topic terminology
m <- LDA(dtm_clean, k = 4, method = "Gibbs", 
         control = list(nstart = 5, burnin = 2000, best = TRUE, seed = 1:5))
topicterminology <- predict(m, type = "terms", min_posterior = 0.025, min_terms = 5)
scores <- predict(m, newdata = dtm, type = "topics")

library(igraph)
library(ggraph)
library(ggplot2)
x_topics <- merge(x, scores, by.x="topic_level_id", by.y="doc_id")
wordnetwork <- subset(x_topics, topic %in% 1 & lemma %in% topicterminology[[1]]$term)
wordnetwork <- cooccurrence(wordnetwork, group = c("topic_level_id"), term = "lemma")
wordnetwork <- graph_from_data_frame(wordnetwork)
ggraph(wordnetwork, layout = "fr") +
  geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink")  +
  geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
  theme_graph(base_family = "Arial Narrow") +
  labs(title = "Words in topic 1 ", subtitle = "Nouns & Adjective cooccurrence")

topicterminology <- predict(m, type = "terms", min_posterior = 0.05, min_terms = 10)
termcorrs <- subset(x_topics, topic %in% 1 & lemma %in% topicterminology[[1]]$term)
termcorrs <- document_term_frequencies(termcorrs, document = "topic_level_id", term = "lemma")
termcorrs <- document_term_matrix(termcorrs)
termcorrs <- dtm_cor(termcorrs)
termcorrs[lower.tri(termcorrs)] <- NA
diag(termcorrs) <- NA
library(qgraph)
qgraph(termcorrs, layout = "spring", labels = colnames(termcorrs), directed = FALSE,
       borders = FALSE, label.scale = FALSE, label.cex = 1, node.width = 0.5)

## Recode tokens to keywords, if it is not in the list of tokens, set to NA
x$mwe <- txt_recode_ngram(x$token, compound = keyw_rake$keyword, ngram = keyw_rake$ngram)
x$mwe <- ifelse(x$mwe %in% keyw_rake$keyword, x$mwe, NA)

## nouns
x$term_noun <- ifelse(x$upos %in% "NOUN", x$token, NA)

## Build document/term/matrix 
dtm <- document_term_frequencies(x, document = "topic_level_id", term = c("term_noun", "mwe"))
dtm <- document_term_matrix(x = dtm)
dtm <- dtm_remove_lowfreq(dtm, minfreq = 3)
m <- LDA(dtm, k = 3, method = "Gibbs", 
         control = list(nstart = 5, burnin = 2000, best = TRUE, seed = 1:5))