Performing Natural Language Processing with R/

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Comparing R Packages for NLP

Comparing R Packages for NLP

Let’s review capabilities of some popular R packages for NLP.

We'll cover the following...

R packages for NLP

Modern programming languages have embraced extensibility, allowing for enhancing and specializing a base language functionality. R exemplifies this philosophy with packages that provide easy-to-use implementations of industry tools and techniques. Regarding natural language processing, developers have provided customized R packages designed around several philosophies and methodologies.

It’s critical to understand NLP concepts, and it’s equally important to choose R packages designed in a way that makes sense for our personal or team goals. There are many packages to choose from, and they all have different strengths. Fortunately, because all of these packages are based on the R programming language, they can be used together and often create compatible data structures.

The following is a brief overview of some of the more popular packages for NLP. This table doesn’t reflect the nuances of each package but only on generalizations. In addition, it doesn’t reflect the dependencies of one package upon another.

Features of R Packages for NLP


Tokenizer

Stop Words

Stemming

Lemmification

POS tagging

Sentiment Analysis

Tf-idf

Visualization

Requirements

coreNLP

✅




✅

✅



Java

koRpus

✅

✅

✅

✅

✅


✅

✅


NLP

✅









openNLP

✅




✅




Java

quanteda

✅

✅

✅

✅

✅

✅

✅

✅


SnowballC



✅







spacyr

✅



✅

✅




Python

text2vec

✅

✅

✅

✅



✅

✅


tidytext

✅

✅



✅

✅

✅


dplyr

tm

✅

✅

✅




✅

✅


udpipe

✅



✅

✅

✅

✅



The following is a brief example of three popular packages: Base R with tm, tidytext, and quanteda.

Base R with tm

The tm package is one of the oldest and best-known NLP ...

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