tm (shorthand for Text Mining Infrastructure in R) provides a framework for text mining applications within R.
The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package has integrated database back-end support to minimize memory demands. An advanced meta data management is implemented for collections of text documents to alleviate the usage of large and with meta data enriched document sets.
The package provides native support for reading in several classic file formats (e.g. plain text, PDFs, or XML files). There is also a plug-in mechanism to handle additional file formats.
The data structures and algorithms can be extended to fit custom demands, since the package is designed in a modular way to enable easy integration of new file formats, readers, transformations and filter operations.
tm provides easy access to preprocessing and manipulation mechanisms such as whitespace removal, stemming, or stopword deletion. Further a generic filter architecture is available in order to filter documents for certain criteria, or perform full text search. The package supports the export from document collections to term-document matrices.
Website: tm.r-forge.r-project.org
Support: FAQ, CRAN
Developer: Ingo Feinerer and contributors
License: GNU General Public License v3.0
tm 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 comprehensive directory of recommended free and open source software. Our carefully curated collection spans every major software category.This directory is part of our ongoing series of informative articles for Linux enthusiasts. It features hundreds of detailed reviews, along with open source alternatives to proprietary solutions from major corporations such as Google, Microsoft, Apple, Adobe, IBM, Cisco, Oracle, and Autodesk. You’ll also find interesting projects to try, hardware coverage, free programming books and tutorials, and much more. Discovered a useful open source Linux program that we haven’t covered yet? Let us know by completing this form. |