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.Add the following code to your website.
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