Introduction to R mallet

David Mimno and Måns Magnusson



To use the mallet R package, we need to use rJava, an R package for using Java within R (to access the mallet Java code). See details at

Next, install the mallet R package from CRAN. To install, simply use install.packages()



Depending on the size of your data, it can be so that you need to increase the Java virtual machine (JVM) heap memory to handle larger corpora. To do this, you need to specify how much memory you want to allocate to the JVM using the Xmx flag. Below is an example of allocating 4 Gb to the JVM.

options(java.parameters = "-Xmx4g")

To load the package, use library().


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()
##  chr [1:6] "English stoplist is the standard Mallet stoplist.\n\nGerman, French, Finnish are borrowed from" ...

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.

## 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
## [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.

## [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.


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()
## [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)
##       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.


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


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., word.weights = topic.words[2,], = 5)
##      term     weight
## 1   years 0.01721053
## 2     war 0.01525148
## 3 america 0.01422937
## 4  people 0.01192963
## 5     men 0.01048164

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]]
## [1] "The last person I want to introduce is Jack Lucas from Hattiesburg,  Mississippi. Jack, would you stand up. Fifty years ago in the sands of Iwo  Jima, Jack Lucas taught and learned the lessons of citizenship. On February  the 20th, 1945, he and three of his buddies encountered the enemy and two  grenades at their feet. Jack Lucas threw himself on both of them. In that  moment he saved the lives of his companions and miraculously in the next  instant a medic saved his life. He gained a foothold for freedom and at the  age of 17, just a year older than his grandson, who's up there with him  today, and his son, who is a West Point graduate and a veteran, at 17, Jack  Lucas became the youngest marine in history and the youngest soldier in  this century to win the Congressional Medal of Honor. All these years  later, yesterday, here's what he said about that day: Didn't matter where  you were from or who you were. You relied on one another. You did it for  your country. We all gain when we give and we reap what we sow. That's at  the heart of this New Covenant. Responsibility, opportunity and  citizenship.  "

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), word.weights = post1975_topic_words[2,], = 5)
##       term weight
## 1  america    123
## 2    years     92
## 3 american     70
## 4   people     67
## 5      war     60

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

Save and load topic states

We can also store our current topic model state to use it for postprocessing. We can store the state file either as a text file or a compressed gzip file.

state_file <- file.path(tempdir(), "temp_mallet_state.gz")
save.mallet.state(topic.model = topic.model, state.file = state_file)

We also store the topic counts per document and remove the old model.

doc.topics.counts <- mallet.doc.topics(topic.model, smoothed=FALSE, normalized=FALSE)


To initialize a model with the sampled topic indicators, one needs to create a new model, load the same data and then load the topic indicators. Unfortunately, setting the alpha parameter vector is currently not possible, so it is not currently possible to initialize the model with the same alpha prior.

new.topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
load.mallet.state(topic.model = new.topic.model, state.file = state_file)

doc.topics.counts[1:3, 1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    0    0
## [2,]    0    7    6    0    0    0
## [3,]    0    0    3    0    0    0
mallet.doc.topics(new.topic.model, smoothed=FALSE, normalized=FALSE)[1:3, 1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    0    0
## [2,]    0    7    6    0    0    0
## [3,]    0    0    3    0    0    0

This vignette gives a first example of using the mallet R package for topic modelling.

Save and load topic models

We can also save Mallet topic models and load them back into R.

model_file <- file.path(tempdir(), "temp_mallet.model"), model_file)
read.topic.model <-

doc.topics.counts[1:3, 1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    0    0
## [2,]    0    7    6    0    0    0
## [3,]    0    0    3    0    0    0
mallet.doc.topics(read.topic.model, smoothed=FALSE, normalized=FALSE)[1:3, 1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    0    0
## [2,]    0    7    6    0    0    0
## [3,]    0    0    3    0    0    0