Analyses of Text using Natural Language Processing and Machine Learning

Transforms text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see < https://www.r-text.org>.


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Reference manual

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install.packages("text")

0.9.10 by Oscar Kjell, a month ago


https://r-text.org/, https://github.com/OscarKjell/text/


Report a bug at https://github.com/OscarKjell/text/issues/


Browse source code at https://github.com/cran/text


Authors: Oscar Kjell [aut, cre] , Salvatore Giorgi [aut] , Andrew Schwartz [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports dplyr, tokenizers, tibble, stringr, tidyr, ggplot2, ggrepel, cowplot, rlang, purrr, magrittr, parsnip, recipes, rsample, reticulate, tune, workflows, yardstick, future, furrr

Suggests knitr, rmarkdown, testthat, rio, glmnet, randomForest, covr, xml2, ranger

System requirements: Python (>= 3.6.0)


See at CRAN