Topic Modelling using LDA with MALLET

MAchine Learning for LanguagE Toolkit, in short MALLET, is a tool written in Java for application of machine learning like natural language processing, document classification, clustering, topic modeling and information extraction to texts. To learn what MALLET has to offer in detail visit this page.

In this post, we see how we can create topic models from a large collection of unlabeled text documents and use the model to infer topics in new documents.

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents.

Topic models use different algorithms to extra topics from a corpus of texts. MALLET uses Gibbs sampling based implementations of Latent Dirichlet Allocation (LDA), Pachinko Allocation and Hierarchical LDA. Check this page out should you wish to know about Topic Modelling in detail.

 Setting up MALLET

Go to the MALLET Mdownload page and download the latest version of MALLET. At the time of writing this post, the latest version is 2.0.8.

Installation on Windows

Ideally, unzip MALLET into your C:. Your path to MALLET will then be something similar C:\mallet-2.0.8. This directory is referred as MALLET directory here onwards. Now you will be able to access MALLET from anywhere on the command prompt using C:\mallet-2.0.8\bin\mallet. To avoid typing full path every time, we can setup an environment variable. To do so, go to Start Menu > Control Panel > System > Advanced System Settings > Environment Variables. Under User variables section, select PATH and click Edit... Go to the end of the text, type ; followed by C:\mallet-2.0.8\bin\ and click Save. Now you will be able to access MALLET with just mallet command. To verify it is working, type the following on the command prompt.

> mallet --help

You should see a list of MALLET commands.

Note: Windows uses back slash (\) as a directory separator while *nix systems use forward slash (/). Examples on this post were run on *nix system (MacOS). Hence, forward slash has been used as a directory separator. You should remember to change them to back slash while running them on Windows Command Prompt.

Installation on *nix (Linux, FreeBSD, Mac OS X)

Unzip MALLET. Typically, you would unzip to paths like /usr/local/bin or /opt. For this post, I have unzipped to /usr/local/opt/mallet-2.0.8This path is referred as MALLET directory here onwards. To avoid typing full path every time, we can set up a path variable. To do so, open ~/.bashrc or ~/.bash_profile (for bash shell) depending upon your distribution and add the following line.

export PATH=$PATH:/usr/local/opt/mallet-2.0.8/bin

To put the changes into effect, type the following in your shell:

$ . ~/[.bashrc | .bash_profile]

You can now access MALLET from anywhere. To verify that it works type:

$ mallet --help

It should list all the MALLET commands.

Working with MALLET

Topic Modelling with MALLET is all about three simple steps:

  1. Import data (documents) into MALLET format
  2. Train your model using the imported data
  3. Use the trained model to infer the topic composition of new document

In this tutorial, we will use the sample data that comes pre-packaged with MALLET. The sample data is found in sample-data directory inside MALLET directory. Before proceeding further, change your current directory to MALLET directory by typing:

$ cd [Your MALLET directory]

MALLET Sample Data

Note: tree command may not be available by default in your system and you might have to install it manually.

Importing Data

There are two methods of importing data into MALLET format.

Importing directories

You would import a directory if the source data consists of many separate files. In this case, each file is considered as one instance. The following command imports all files from a directory sample-data/web/en and converts to single MALLET file named train.mallet  in your current directory.

$ mallet import-dir \
	--input sample-data/web/en/ \
	—-output train.mallet \
	—-remove-stopwords TRUE \
	—-keep-sequence TRUE

Here, options except input and output are optional. You can also pass more than one directories; directories’ name should be separated by space.

remove-stopwords TRUE remove the words such as aantheif and so on. By default, MALLET’s default English dictionary of stop words is used. If you wish to supply your own list of stopwords, which you would custom to your application, you can do so by passing the file name to stoplist-file option.  The stoplist contains stop words separated by space, a tab character or a line break.

The MALLET toolkit requires keep-sequence option set to TRUE  for topic modeling.

To see more options type

$ mallet import-dir --help

In this tutorial, we are using this method.

Importing a file

You’d use this method if all of your data is in a single file, one instance per line, in the following format:

[instance_name] [label] [text without line breaks]

instance_name uniquely identifies each instance. For topic modelling, instance_name and label can be same.

You’d type the following command.

$ mallet import-file \
	--input [file_name] \
	—-output train.mallet \

All the options that apply to import-dir also apply to import-file.

Note: If you are importing extremely large file or file collections, you might get ‘Exception in thread “main” java.lang.OutOfMemoryError: Java heap space’ error. If you encounter this error, you have run into your memory limit which is 1 GB by default. To update the limit, open the file named mallet (or mallet.bat in case of Windows) in ‘bin’ directory inside mallet directory with a text editor, find the line ‘MEMORY=1g’ and update the value ‘1g’ to higher values like ‘2g’, ‘4g’ or higher depending on your system’s RAM.

 Training the model

After you have imported documents into MALLET format, you need to build a topic model. The following command takes the file train.mallet which we created in the previous section, create 5 topics (topics.txt) and calculates the topic proportion for each instance (topic-composition.txt).

$ mallet train-topics \
	-—input train.mallet \
	—-inferencer-filename inferencer.mallet \
	-—num-topics 5 \
	-—output-topic-keys topics.txt \
	-—output-doc-topics topic-composition.txt

If you open topics.txt, you will see 5 lines.  In each line, the first number is the topic number, the second number is the indication of the weight of that topic and the words following them are the most frequently occurring words that fall into that topic.

topic-composition.txt file lists the composition of each instance or document under the topics listed in topic.txt. In each line, the first value is the instance number, the second value is an instance or document name and the numbers following are the weight of corresponding topics in topics.txt.

Mallet Topic Composition Output

To see more options, type

$ mallet train-topics --help

Deciding the number of topics

There is no natural number of topics. To find the suitable number of topics, we have to run train-topics with a varying number of topics and see how the topic composition break down. If the majority of the words group to a very narrow number of topics, we need to increase the number of topics. On the other hand, if related words fall under different topics, the setting is too broad and we need to narrow it down by reducing the number of topics.

Inferring topic composition of new documents

To infer the topic composition of new documents, you first need to import the new documents into MALLET format similar to what we did in the first section.

$ mallet [import-dir | import-file] \
	--input [directory_name | file_name] \
	—-output new.mallet \
	—-remove-stopwords TRUE \
	—-keep-sequence TRUE \
	--use-pipe-from train.mallet

Notice user-pipe-from option though. It is very important that you include this option at this stage. This option is used to make sure that the new data is compatible with our training data i.e. new data and training data have same alphabet mappings.

Finally, the following command infers the topic composition of the new documents and stores it in new-topic-composition.txt.

$ mallet infer-topics \
	--input new.mallet \
	--inferencer inferencer.mallet \
	--output-doc-topics new-topic-composition.txt

To see more options type

$ mallet infer-topics --help

This will infer the topic composition of new documents and save it to new-topic-composition.txt.

Please leave your comments or any query you have in the comment section below. I will be happy to help.


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