The goal of simpletextr is to create simple Natural Language Processing functions that take care of the pre-processing and allow the user to focus on the task and the output. This would be most useful where the objective is to extract something from a vector of text.

There are some common NLP or text-mining tasks to begin with could be entity extraction (people, places), keyword extraction and perhaps even topic modelling or text classification.

The functions would all be self-descriptive so: extract_place(), extract_people(), extract_topics() etc.

Inputs would be simple vectors of text and outputs a vector or list of the same length. So this could easily slot into a tidy workflow:

The most complex part of NLP is the pre-processing. But I suspect (hope) it would be possible to setup a robust and generic process. And I think for 90% of use cases a generic pre-processing with only a few options would be sufficient.

The question I have is whether or not something like this already exists, I will check.


You can install the released version of simpletextr from Github with:


Simple Cleaning

This is a basic example which shows you how to clean a small twitter dataset from #ozunconf18:


text <- ozunconf18_tweets$text

cleaned <- clean_text(text, stop_words = 'rt')

Now we can see the difference between a raw tweet:

RT @fidlerfm: Excited about tomorrow’s talk by Franca Agnoli on failures to replicate stereotype threat effects in girls’ mathematics perfo…

and the same tweet cleaned:

fidlerfm excited tomorrows talk franca agnoli failures replicate stereotype threat effects girls mathematics perfo…

Simple Outputs

Term Table

term count
ozunconf 24
moving 23
https 22
day 21
nj 13

Comparison with Tidytext

After reading Text Mining with R it appears many of the objectives of this package are already met by the tidytext package. There is still some value in a package such as simpletextr. But better understanding of current tools is required for this value to be identified.