quanteda: An R package for the quantitative analysis of textual data
By Kenneth Benoit, Kohei Watanabe, Haiyan Wang, P. Nulty, Adam Obeng, Stefan Müller, Akitaka Matsuo. Journal of Open Source Software, 2018
1,128 citations recorded by the source corpus.
quanteda is an R package providing a comprehensive workflow and toolkit for natural language processing tasks such as corpus management, tokenization, analysis, and visualization. It has extensive functions for applying dictionary analysis, exploring texts using keywords-in-context, computing document and feature similarities, and discovering multi-word expressions through collocation scoring. Based entirely on sparse operations,it provides highly efficient methods for compiling document-feature matrices and for manipulating these or using them in further quantitative analysis. Using C++ and multi-threading extensively, quanteda is also considerably faster and more efficient than other R and Python packages in processing large textual data. The package is designed for R users needing to apply natural language processing to texts,from documents to final analysis. Its capabilities match or exceed those provided in many end-user software applications, many of which are expensive and not open source. The package is therefore of great benefit to researchers, students, and other analysts with fewer financial resources. While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps. By emphasizing consistent design, furthermore, quanteda lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/f1a210051a89f06fdb90f50a09f78ddb87bf308f
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In Artificial Intelligence
- Dustin Tingley
- Brandon M Stewart
- Margaret E. Roberts
- Stuart Soroka
- In Song Kim
- Lambda Moses
- Lior S. Pachter
In Information Systems
Citations in the corpus
- Politicization and Polarization in COVID-19 News Coverage (2020, methodology): “The selection of articles and all subsequent analyses were conducted using the quanteda package in R (Benoit et al., 2018).”
- stm: An R Package for Structural Topic Models (2019, methodology): “Using the quanteda package A useful external tool for handling text processing is the quanteda package (Benoit et al. 2018), which makes it easy to import text and associated metadata, prepare the texts for processing, and convert the documents into a document-term matrix.”