textmodel_wordvector objects from
wordvector v0.6.0.textmodel_lss.tokens() to use
wordvector::textmodel_word2vec() as the underlying
engine.w to k in
textmodel_lss.fcm() to make it consistent with other
methods.smooth_lss().textplot_*() for upcoming
ggplot2.as.textmodel_lss() when a
textmodel_wordvector object is given.sampling to textplot_terms() to
improve highlighting of words when the distribution of polarity scores
is asymmetric.textmodel_wordvector objects
from the wordvector package in
as.textmodel_lss().auto_weight in
textmodel_lss().textplot_simil().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.
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