R package quanteda: Quantitative Analysis of Textual Data. A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
- Samuel Borms, David Ardia, Keven Bluteau, Kris Boudt, Jeroen Van Pelt, Andres Algaba: The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment (2021) not zbMATH
- Chung-hong Chan; Marius Sältzer: oolong: An R package for validating automated content analysis tools (2020) not zbMATH
- Daneshgar, Neda; Sarmad, Majid: \textttword.alignment: an \textttRpackage for computing statistical word alignment and its evaluation (2020)
- John D. Boy: textnets: A Python package for text analysis with networks (2020) not zbMATH
- Jonas Rieger: ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations (2020) not zbMATH
- Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
- Benoit K, Watanabe K, Wang H, Nulty P, Obeng A, Müller S, Matsuo A: quanteda: An R package for the quantitative analysis of textual data (2018) not zbMATH
- Julia Silge; David Robinson: tidytext: Text Mining and Analysis Using Tidy Data Principles in R (2016) not zbMATH