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Changes in v1.4.0

• Change the default value to max_highlighted = 1000 in textplot_terms().
• Add ... to customize text labels to textplot_terms().
• Highlight words in different colors when a dicitonary is passed to highlighted.
• Add mode = "predict" and remove = FALSE to bootstrap_lss().

Changes in v1.3.2

• Fix the error in textplot_terms() when the frequency of terms are zero (#85).

Changes in v1.3.1

• Fix the range of scores when cut is used.
• Add bootstrap_lss() as an experimental function.

Changes in v1.3.0

• Add cut to predict.
• Move examples to the new package website: http://koheiw.github.io/LSX.
• Rename “rescaling” to “rescale” for simplicity and consistency.
• Improve random sampling of words to highlight in textplot_terms() to avoid congestion.

Changes in v1.2.0

• Add group_data to textmodel_lss() to simplify the workflow.
• Add max_highlighted to textplot_terms() to automatically highlight polarity words.

Changes in v1.1.4

• Update as.textmodel_lss() to avoid errors in textplot_terms() when terms is used.

Changes in v1.1.3

• Restore examples for textmodel_lss().
• Defunct char_keyness() that has been deprecated for long.

Changes in v1.1.2

• Update examples to pass CRAN tests.

Changes in v1.1.1

• Add min_n to predict() to make polarity scores of short documents more stable.

Changes in v1.1.0

• Add as.textmodel_lss() for textmodel_lss objects to allow modifying existing models.
• Allow terms in textmodel_lss() to be a named numeric vector to give arbitrary weights.

Changes in v1.0.2

• Add the auto_weight argument to textmodel_lss() and as.textmodel_lss() to improve the accuracy of scaling.
• Remove the group argument from textplot_simil() to simplify the object.
• Make as.seedwords() to accept multiple indices for upper and lower.

Changes in v1.0.0

• Add max_count to textmodel_lss.fcm() that will be passed to x_max in rsparse::GloVe$new().
• Add max_words to textplot_terms() to avoid overcrowding.
• Make textplot_terms() to work with objects from textmodel_lss.fcm().
• Add concatenator to as.seedwords().

Changes in v0.9.9

• Correct how textstat_context() and char_context() computes statistics.
• Deprecate char_keyness().

Changes in v0.9.8

• Stop using functions and arguments deprecated in quanteda v3.0.0.

Changes in v0.9.7

• Make as.textmodel_lss.matrix() more reliable.
• Remove quanteda.textplots from dependencies.

Changes in v0.9.6

• Updated to reflect changes in quanteda (creation of quanteda.textstats).

Changes in v0.9.4

• Fix char_context() to always return more frequent words in context.
• Experimental textplot_factor() has been removed.
• as.textmodel_lss() takes a pre-trained word-embedding.

Changes in v0.9.3

• Add textstat_context() and char_context() to replace char_keyness().
• Make the absolute sum of seed weight equal to 1.0 in both upper and lower ends.
• 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.
• Add engine to smooth_lss() to apply locfit() to large datasets.

Changes in v0.9.2

• Updated unit tests for the new versions of stringi and quanteda.

Changes in v0.9.0

• Renamed from LSS to LSX for CRAN submission.

Changes in v0.8.7

• Added 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.