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as.textmodel_lss() for objects from the
wordvector package.textplot_terms().as.textmodel_lss.textmodel_lss().group to smooth_lss() to smooth LSS
scores by group.optimize_lss() as an experimental function.max_highlighted = 1000 in
textplot_terms().... to customize text labels to
textplot_terms().highlighted.mode = "predict" and remove = FALSE to
bootstrap_lss().textplot_terms() when the frequency of
terms are zero (#85).cut is used.bootstrap_lss() as an experimental function.cut to predict.textplot_terms() to avoid congestion.group_data to textmodel_lss() to
simplify the workflow.max_highlighted to textplot_terms() to
automatically highlight polarity words.as.textmodel_lss() to avoid errors in
textplot_terms() when terms is used.textmodel_lss().char_keyness() that has been deprecated for
long.min_n to predict() to make polarity
scores of short documents more stable.as.textmodel_lss() for textmodel_lss objects to
allow modifying existing models.terms in textmodel_lss() to be a
named numeric vector to give arbitrary weights.auto_weight argument to
textmodel_lss() and as.textmodel_lss() to
improve the accuracy of scaling.group argument from
textplot_simil() to simplify the object.as.seedwords() to accept multiple indices for
upper and lower.max_count to textmodel_lss.fcm() that
will be passed to x_max in
rsparse::GloVe$new().max_words to textplot_terms() to avoid
overcrowding.textplot_terms() to work with objects from
textmodel_lss.fcm().concatenator to as.seedwords().textstat_context() and
char_context() computes statistics.char_keyness().as.textmodel_lss.matrix() more reliable.char_context() to always return more frequent words
in context.textplot_factor() has been removed.as.textmodel_lss() takes a pre-trained
word-embedding.textstat_context() and char_context()
to replace char_keyness().textplot_terms() takes glob patterns in character
vector or a dictionary object.char_keyness() no longer raise error when no patter is
found in tokens object.engine to smooth_lss() to apply
locfit() to large datasets.textplot_terms() to improve visualization of
model terms.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.