person(given = "Zahier",
family = "Nasrudin",
role = c("aut", "cre"),
email = "zahiernasrudin@gmail.com",
comment = c(ORCID = "0000-0002-7060-776X"))
Text mining application (Bahasa Malaysia).
Zahier Nasrudin [aut, cre] (<https://orcid.org/0000-0002-7060-776X>)

It is built to handle Bahasa Malaysia text. We provide
functions and data sets that will help handling Bahasa Malaysia to be much
easier. For word stemming in particular, we will find the Malay words in a dictionary and
then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah,
Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce,
Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi
(2017) <https://ijrest.net/vol-4-issue-12.html> . A dictionary of Malay
words provided in this package can be used as a dictionary to perform
word stemming.

It is designed to work with text written in Bahasa Malaysia. We provide
functions and data sets that will make working with Bahasa Malaysia text much easier.
For word stemming in particular, we will look up the Malay words in a dictionary and
then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah,
Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce,
Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi
(2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes
a dictionary of Malay words that may be used to perform word stemming.

A wrapper around the Java machine learning tool MALLET
This package allows you to train topic models in mallet and load results directly into R.

An R Wrapper for the Java Mallet Topic Modeling Toolkit
c(
person("Måns", "Magnusson", email = "mons.magnusson@gmail.com", role = c("cre", "aut"),
comment = c(ORCID = "0000-0002-0296-2719")),
person("David", "Mimno", role = c("aut", "cph"),
comment = c(ORCID = "0000-0001-7510-9404"))
)
An R interface for the Java Machine Learning for Language Toolkit (mallet)
<http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such
as Latent Dirichlet Allocation. We can use the R package to read textual
data into mallet from R objects, run the Java implementation of mallet
directly in R, and extract results as R objects. The Mallet toolkit
has many functions, this wrapper focuses on the topic modeling sub-package
written by David Mimno. The package uses the rJava package to connect to a
JVM.