Introduction to R mallet

Reading data into R

There are multiple ways to read text data into R. A simple way is to read individual text files into a character vector. Below is an example of reading the different stop list txt files that come with the mallet package into R as a character vector (that can be used by the mallet R package as data).

# Note this is the path to the folder where the stoplists are stored in the R package.
# Change this path to another directory to read other txt files into R.
directory <- system.file("stoplists", package = "mallet")

files_in_directory <- list.files(directory, full.names = TRUE)

txt_file_content <- character(length(files_in_directory))
for(i in seq_along(files_in_directory)){
  txt_file_content[i] <- paste(readLines(files_in_directory[i]), collapse = "\n")
}
# We can check the content with str()
str(txt_file_content)

##  chr [1:6] "aber\nals\nam\nan\nauch\nauf\naus\nbei\nbin\nbis\nbist\nda\ndadurch\ndaher\ndarum\ndas\ndaß\ndass\ndein\ndeine\"| __truncated__ ...

We will now use the example data set of the State of the Union addresses from 1946 to 2000 that is included with the mallet R package as a data.frame. This data can be accessed as follows.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data(sotu)
sotu[["text"]][1:2]

## [1] "To the Congress of the United States:  "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     
## [2] "A quarter century ago the Congress decided that it could no longer consider  the financial programs of the various departments on a piecemeal basis.  Instead it has called on the President to present a comprehensive Executive  Budget. The Congress has shown its satisfaction with that method by  extending the budget system and tightening its controls. The bigger and  more complex the Federal Program, the more necessary it is for the Chief  Executive to submit a single budget for action by the Congress.  "

Mallet also comes with five different stop list files (see above). We can access the path to these lists as follows.

mallet_supported_stoplists()

## [1] "de" "en" "fi" "fr" "jp"

stopwords_en_file_path <- mallet_stoplist_file_path("en")

Training topic models

As a first step, we need to create an LDA trainer object and supply the trainer with documents. We start by creating a mallet instance list object. This function has a few extra options (whether to lowercase or how we define a token). See ?mallet.import for details.

sotu.instances <- 
  mallet.import(id.array = row.names(sotu), 
                text.array = sotu[["text"]], 
                stoplist = stopwords_en_file_path,
                token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

If the data is already cleaned and we want to use the index of text.array, we can supply the text.array.

sotu.instances.short <- 
  mallet.import(text.array = sotu[["text"]])

It is also possible to supply stop words as a character vector.

stop_vector <- readLines(stopwords_en_file_path)
sotu.instances.short <- 
  mallet.import(text.array = sotu[["text"]], 
                stoplist = stop_vector)

We first need to create a topic trainer object to fit a model.

topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)

Load our documents. We could also pass in the filename of a saved instance list file we build from the command-line tools.

topic.model$loadDocuments(sotu.instances)

We use the method getVocabulary() to get the model’s vocabulary. The vocabulary may be helpful in further curating the stopword list.

vocabulary <- topic.model$getVocabulary()
head(vocabulary)

## [1] "congress" "united"   "states"   "quarter"  "century"  "ago"

Similarly, we can access the word and document frequencies with mallet.word.freqs().

word_freqs <- mallet.word.freqs(topic.model)
head(word_freqs)

##       word word.freq doc.freq
## 1 congress      1025      879
## 2   united       508      426
## 3   states       557      480
## 4  quarter        16       15
## 5  century       166      155
## 6      ago       179      171

To optimize hyperparameters ( and ) every 20 iterations, after 50 burn-in iterations, we set alpha optimization as follows.

topic.model$setAlphaOptimization(20, 50)

Now train a model. Note that hyperparameter optimization is on by default. We can specify the number of iterations. Here we’ll use a large-ish round number.

topic.model$train(200)

We can also run through a few iterations where we pick the best topic for each token rather than sampling from the posterior distribution.

topic.model$maximize(10)

Analysis of a topic model

To analyze our corpus using our model, we usually want to access the probability of topics per document and the probability of words per topic. By default, these functions return raw word counts. Here we want probabilities, so we normalize and add “smoothing” so that nothing has exactly 0 probability.

doc.topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic.words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)

What are the top words in topic 2? Notice that R indexes from 1 and Java from 0, so this will be the topic that mallet called topic 1.

mallet.top.words(topic.model, word.weights = topic.words[2,], num.top.words = 5)

##        term      weight
## 1    people 0.020313973
## 2  children 0.018424672
## 3      work 0.016872746
## 4      make 0.011879593
## 5 education 0.009113116

Show the largest document with at least 50% tokens belonging to topic 2. Note, since the model is not identified, you might end up with another topic if you run the same code.

docs <- which(doc.topics[,2] > 0.50)
doc_size <- nchar(sotu[["text"]])[docs]
idx <- docs[order(doc_size, decreasing = TRUE)[1]]
sotu[["text"]][idx]

## [1] "Goals 2000 links world-class standards to grassroots reforms and I hope  Congress will pass it without delay. Our school to work initiative will for  the first time link school to the world of work, providing at least one  year of apprenticeship beyond high school. After all, most of the people  we're counting on to build our economic future won't graduate from college.  It's time to stop ignoring them and start empowering them. We must  literally transform our outdated unemployment system into a new  reemployment system. The old unemployment system just sort of kept you  going while you waited for your old job to come back. We've got to have a  new system to move people into new and better jobs because most of those  old jobs just don't come back. And we know that the only way to have real  job security in the future, to get a good job with a growing income, is to  have real skills and the ability to learn new ones. So we've got to  streamline today's patchwork of training programs and make them a source of  new skill for our people who lose their jobs. Reemployment, not  unemployment, must become the centerpiece of our economic renewal. I urge  you to pass it in this session of Congress.  "

We can also study the topics and how the differ in different parts of the corpus, for example in different time periods.

post1975_topic_words <- mallet.subset.topic.words(topic.model, sotu[["year"]] > 1975)
mallet.top.words(topic.model, word.weights = post1975_topic_words[2,], num.top.words = 5)

##       term weight
## 1   people    255
## 2     work    223
## 3 children    222
## 4     make    151
## 5     it's    129

Another functionality included in the mallet R package is to (hierarchically) cluster the topics to assess what topics that are “closer” to each other. Use ?mallet.topic.hclust to see further details on how to cluster topics.

topic_labels <- mallet.topic.labels(topic.model, num.top.words = 2)
topic_clusters <- mallet.topic.hclust(doc.topics, topic.words, balance = 0.5)
plot(topic_clusters, labels=topic_labels, xlab = "", )