The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
library(TextAnalysisR)
tokens <- quanteda::tokens(SpecialEduTech$abstract[1:5])
dispersion <- calculate_lexical_dispersion(
tokens,
terms = c("learning", "instruction")
)
head(dispersion)## doc_id term position doc_length
## 1 text2 learning 0.5200000 25
## 2 text3 learning 0.3714286 35
## 3 text3 instruction 0.3142857 35
## 4 text3 instruction 0.6857143 35
## 5 text5 learning 0.7904762 105
Python enables features: NLP with spaCy, embeddings, and neural sentiment analysis.
setup_python_env() automatically:
textanalysisr-envspacy and pdfplumberen_core_web_sm)Uses virtualenv (or conda if available).
Run check_python_env() to verify the environment.
Set preferred environment in .Rprofile with
Sys.setenv(RETICULATE_PYTHON_ENV = "textanalysisr-env"),
then restart R.
Avoid OneDrive paths. Use
setup_python_env(envname = "textanalysisr-env").
The default en_core_web_sm model is installed
automatically. For word vectors (similarity), the medium model is 91 MB
and the large model is 560 MB:
For embeddings and neural sentiment:
Use reticulate::py_config() and
reticulate::virtualenv_list() to inspect the active
Python.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.