---
title: "UDPipe Natural Language Processing - Topic Modelling Use Cases"
author: "Jan Wijffels"
date: "`r Sys.Date()`"
output:
  html_vignette:
    fig_caption: false
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{UDPipe Natural Language Processing - Topic Modelling Use Cases}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE, cache=FALSE}
options(width = 1000)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, comment = NA, eval = (require(topicmodels)))
```

## 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. 

```{r}
library(udpipe)
ud_model <- udpipe_download_model(language = "french")
```

```{r, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, comment = NA, eval = !ud_model$download_failed)
```

```{r}
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. 

```{r}
str(x)
```

## 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.

```{r}
## 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)
```

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

```{r}
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.

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

#### 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.

```{r}
predict(m, type = "terms", min_posterior = 0.05, min_terms = 3)
```


### 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.

```{r}
## 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

```{r eval=FALSE}
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")
```

![](coocurrence-example3.png)


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

```{r eval=FALSE}
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)
```

![](correlation-example4.png)

## 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.

```{r}
## 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.

```{r}
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

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

### Other option to build document term matricis

In the above exercise, nouns which were part of a compound multi-word-expression (mwe) were replaced by the multi-word-expression. Sometimes however, you want to keep the noun as well as the multi-word expression in the topic model even if the noun is always part of a multi-word expression. You can do this as follows.

```{r}
## 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))
```


## Summarising topics

The textrank R package allows you to easily summarise text, it integrates well with this udpipe R package. It's especially suited for finding a the most relevant sentences of documents of a certain (LDA) topic. More details on that package, see https://CRAN.R-project.org/package=textrank. 


## Support in text mining

Need support in text mining. 
Contact BNOSAC: http://www.bnosac.be


```{r, results='hide', echo=FALSE}
invisible(if(file.exists("french-gsd-ud-2.4-190531.udpipe")) file.remove(c("french-gsd-ud-2.4-190531.udpipe")))
```