UDPipe Natural Language Processing - Topic Modelling Use Cases

UDPipe for Topic Modelling

In order to get the most out of the package, we will show how to use the outcome of the annotation to improve topic modelling. The main use cases of using the outcome of the annotation is to

build topic models only on specific parts of speech tags
build topic models on compound keyword instead of just unigrams
allow to easily summarise topics

Let’s show this.

Start with annotating

Let’s start by annotating some text in French. The annotated data.frame can next be used for basic text analytics.

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)

The resulting data.frame has a field called upos which is the Universal Parts of Speech tag and also a field called lemma which is the root form of each token in the text. These 2 fields give us a broad range of topic modelling possibilities.

str(x)

'data.frame':   30081 obs. of  14 variables:
 $ doc_id       : chr  "47860059" "47860059" "47860059" "47860059" ...
 $ paragraph_id : int  1 1 1 1 1 1 1 1 1 1 ...
 $ sentence_id  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ sentence     : chr  "Quelle excellent week end - Merci a David pour sa confiance, merci a son appart d etre aussi chouette, merci au"| __truncated__ "Quelle excellent week end - Merci a David pour sa confiance, merci a son appart d etre aussi chouette, merci au"| __truncated__ "Quelle excellent week end - Merci a David pour sa confiance, merci a son appart d etre aussi chouette, merci au"| __truncated__ "Quelle excellent week end - Merci a David pour sa confiance, merci a son appart d etre aussi chouette, merci au"| __truncated__ ...
 $ token_id     : chr  "1" "2" "3" "4" ...
 $ token        : chr  "Quelle" "excellent" "week" "end" ...
 $ lemma        : chr  "quel" "excellent" "weekend" "end" ...
 $ upos         : chr  "DET" "ADJ" "NOUN" "NOUN" ...
 $ xpos         : chr  NA NA NA NA ...
 $ feats        : chr  "Gender=Fem|Number=Sing" "Gender=Masc|Number=Sing" "Gender=Masc|Number=Sing" "Gender=Fem|Number=Sing" ...
 $ head_token_id: chr  "3" "3" "6" "3" ...
 $ dep_rel      : chr  "det" "amod" "nsubj" "nmod" ...
 $ deps         : chr  NA NA NA NA ...
 $ misc         : chr  NA NA NA NA ...

Basic topic modelling

You can easily go from this annotated data.frame to a document-term-matrix which is used by a lot of other text mining R packages. In this case, we will build topics at the sentence level.

The advantage of this package over other packages is that

topic modelling can be done directly on the right terms as these can be easily identified with the Parts of Speech tag: mostly you are only interested in nouns and verbs or only in finding topics adjectives if you are interested in sentiment clustering.
so you don’t have to build a long list of stopwords any more
and moreover you can work based on the lemma instead of the plain token, making the words resemble more each other
you can easily include compound keywords (see later in this vignette)

Topic modelling only on specific POS tags

Below we fit a topic model on the nouns only.

## 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)

   doc_id      term freq
1:   1860   weekend    1
2:   1860       end    1
3:   1860 confiance    1
4:   1860    appart    1
5:   1860      être    2
6:   1860  chouette    1

## 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))

15minute     Hote     acce  accueil  adresse   airbnb 
       5        8       11      133       38        5

## 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)

Once we have our document/term/matrix, topic modelling is simple

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

Predict new documents

Furthermore, we can also easily use the model to predict to which topic a new document belongs to. And the predict function will give correctly NA values for documents for which we don’t have terms which are used in the model which is a pain in other R packages. It also allows to provide labels to each topic and gives the difference in topic probability to the next best topic which is usefull to see how clear-cut your topics are.

scores <- predict(m, newdata = dtm, type = "topics", 
                  labels = c("labela", "labelb", "labelc", "xyz"))
str(scores)

'data.frame':   1763 obs. of  9 variables:
 $ doc_id            : chr  "1860" "1293" "1294" "1295" ...
 $ topic             : int  3 NA NA NA NA NA 3 3 NA NA ...
 $ topic_label       : chr  "labelc" NA NA NA ...
 $ topic_prob        : num  0.265 NA NA NA NA ...
 $ topic_probdiff_2nd: num  0.0196 NA NA NA NA ...
 $ topic_labela      : num  0.245 NA NA NA NA ...
 $ topic_labelb      : num  0.245 NA NA NA NA ...
 $ topic_labelc      : num  0.265 NA NA NA NA ...
 $ topic_xyz         : num  0.245 NA NA NA NA ...

Interpret topics

To interpret the topic model and to give appropriate labels, you can get the most emitted terms for each topic with a minimal term emittance probability and the minimum number of terms to show. It is also usefull to look at the textrank package (https://CRAN.R-project.org/package=textrank) which allows to summarise text and can be used to find the most relevant sentences within each topic.

predict(m, type = "terms", min_posterior = 0.05, min_terms = 3)

$topic_001
          term       prob
1   experience 0.18725490
2 proprietaire 0.11862745
3     dejeuner 0.07941176
4     couchage 0.06960784
5   equipement 0.05980392

$topic_002
     term       prob
1 plaisir 0.22132353
2    goût 0.15514706
3     cas 0.07426471
4    Hote 0.05955882
5    cafe 0.05955882

$topic_003
         term   prob
1 emplacement 0.1208
2     endroit 0.1208
3     semaine 0.1128
4       chose 0.0728
5       merci 0.0648
6    occasion 0.0648
7       envie 0.0568

$topic_004
        term       prob
1   probleme 0.15981308
2     besoin 0.11308411
3         et 0.10373832
4     moment 0.10373832
5    annonce 0.07570093
6 decoration 0.06635514
7    parking 0.06635514

Doing the same on the adjectives + nouns

If you want to do the same on adjectives + nouns, just change the above code where it says upos %in% c("NOUN") in upos %in% c("NOUN", "ADJ") and you are ready.

## 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")

Topic visualisation

Once you have topics, visualising these can also be easily done with the igraph and ggraph packages. Below one possible plot is shown. It shows for a certain topic the co-occurrence of terms

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")

Another possibility is showing correlations among the terms driving the topic for only documents of that topic.

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)

Include keywords in topic models

You mostly get better, more interpretable results in topic models if you include compound keywords in the model. Let’s show the steps how you can accomplish this.

First get keywords using either the keywords_rake, keywords_phrases, keywords_collocation functions or with functionality from the textrank R package.
Use function txt_recode_ngram to recode words to keywords. This will replace a sequence of words with its compound multi-word expression by first starting with words which contain more terms.
Build the document-term-matrix to build a topic model

In the below example, we are building a topic model on all nouns, all compound keywords which consists of nouns and adjectives and on all identified noun phrases.

## Find keywords with RAKE 
keyw_rake <- keywords_rake(x, 
                           term = "token", group = c("doc_id", "paragraph_id", "sentence_id"), 
                           relevant = x$upos %in% c("NOUN", "ADJ"), 
                           ngram_max = 3, n_min = 5)
## Find simple noun phrases
x$phrase_tag <- as_phrasemachine(x$upos, type = "upos")
keyw_nounphrases <- keywords_phrases(x$phrase_tag, term = x$token, 
                                     pattern = "(A|N)*N(P+D*(A|N)*N)*", is_regex = TRUE, 
                                     detailed = FALSE)
keyw_nounphrases <- subset(keyw_nounphrases, ngram > 1)

## Recode terms to keywords
x$term <- x$token
x$term <- txt_recode_ngram(x$term, 
                           compound = keyw_rake$keyword, ngram = keyw_rake$ngram)
x$term <- txt_recode_ngram(x$term, 
                           compound = keyw_nounphrases$keyword, ngram = keyw_nounphrases$ngram)
## Keep keyword or just plain nouns
x$term <- ifelse(x$upos %in% "NOUN", x$term,
                 ifelse(x$term %in% c(keyw_rake$keyword, keyw_nounphrases$keyword), x$term, NA))

## Build document/term/matrix
dtm <- document_term_frequencies(x, document = "topic_level_id", term = "term")
dtm <- document_term_matrix(x = dtm)
dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)

Once we have our document/term/matrix, topic modelling is simple. Keep in mind that you need to tune your topic model, which is not done below. See the topicmodels and ldatuning R package which show you how to do that.

m <- LDA(dtm, k = 3, method = "Gibbs", 
         control = list(nstart = 5, burnin = 2000, best = TRUE, seed = 1:5))

You’ll see that the topic model now includes keywords

topicterminology <- predict(m, type = "terms", min_posterior = 0.10, min_terms = 3)
topicterminology

$topic_001
      term       prob
1     tout 0.08531097
2    passe 0.06280687
3 agreable 0.04439444

$topic_002
  term       prob
1 tres 0.18747444
2  pas 0.09615647
3  ete 0.04504566

$topic_003
           term       prob
1   appartement 0.15585207
2         situe 0.09286101
3 tres agreable 0.05560824

UDPipe Natural Language Processing - Topic Modelling Use Cases

Jan Wijffels

2022-03-23