textclean is a collection of tools to clean and normalize text. Many of these tools have been taken from the qdap package and revamped to be more intuitive, better named, and faster.
Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents.
This is free and open source software.
Website: github.com/trinker/textclean
Support:
Developer: Tyler Rinker
License: GNU General Public License v2.0
textclean is written in R. Learn R with our recommended free books and free tutorials.
Related Software
| R Natural Language Processing Tools | |
|---|---|
| tidytext | Text mining using dplyr, ggplot2, and other tidy tools |
| quanteda | R package for Quantitative Analysis of Textual Data |
| text2vec | Framework with API for text analysis and natural language processing |
| wordcloud | Create attractive word clouds |
| tm | Text Mining Infrastructure in R |
| srtringi | Fast and portable character string processing in R |
| Stringr | String manipulation in R |
| UDPipe | Tokenization, Tagging, Lemmatization and Dependency Parsing |
| tokenizers | Convert natural language text into tokens |
| spacyr | R wrapper around the Python spaCy package |
| Word Vectors | Build and explore embedding models |
| syuzhet | Extraction of sentiment and sentiment-based plot arcs from text |
| textTinyR | Text processing for small or big data |
| sentimentr | Dictionary based sentiment analysis |
| textclean | Collection of tools to clean and normalize text |
| TALL | Explore, model, and visualize textual data |
| corpustools | Various tools for analyzing text corpora |
| topicmodels | Interface to LDA and CTM models |
| text | Analyzing natural language with transformers-based large language models |
| RTextTools | Automatic text classification via supervised learning |
Read our verdict in the software roundup.
 Explore our carefully curated directory of recommended free and open source software, covering every major software category.The directory forms part of our extensive collection of articles for Linux enthusiasts. It includes hundreds of detailed reviews, together with free and open source alternatives to proprietary software from companies such as Google, Microsoft, Apple, Adobe, IBM, Cisco, Oracle, and Autodesk. LinuxLinks also covers interesting projects worth exploring, Linux-compatible hardware, free programming books and tutorials, and much more. Know a useful free and open source Linux application that we haven’t covered? Tell us about it using our submission form. |
Please read our Comment Policy before commenting.