- Updates to documentation (#102), README, and vignettes.
- Add tokenizing by character shingles thanks to Kanishka Misra (#105).
- Fix tests for skip grams thanks to Lincoln Mullen (#106).
- Updated more docs/tests so package can build on R-oldrel. (Still trying!)
unnest_tokens can now unnest a data frame with a list column (which formerly threw the error
unnest_tokens expects all columns of input to be atomic vectors (not lists)). The unnested result repeats the objects within each list. (It’s still not possible when
collapse = TRUE, in which tokens can span multiple lines).
get_tidy_stopwords() to obtain stopword lexicons in multiple languages in a tidy format.
- Add a dataset
nma_words of negators, modals, and adverbs that affect sentiment analysis (#55).
- Updated various vignettes/docs/tests so package can build on R-oldrel.
- Change how
NA values are handled in
unnest_tokens so they no longer cause other columns to become
- Update tidiers and casters to align with quanteda v1.0 (#87).
- Handle input/output object classes (such as
data.table) consistently (#88).
- Fix tidier for quanteda dictionary for correct class (#71).
- Add a pkgdown site.
- Convert NSE from underscored function to tidyeval (
bind_tf_idf, all sparse casters) (#67, #74).
- Added tidiers for topic models from the
stm package (#51).
get_sentiments now works regardless of whether
tidytext has been loaded or not (#50).
unnest_tokens now supports data.table objects (#37).
to_lower parameter in
unnest_tokens to work properly for all tokenizing options.
glance.corpus, tests, and vignette for changes to quanteda API
- Removed the deprecated
pair_count function, which is now in the in-development widyr package
- Added tidiers for LDA models from the
- Added the Loughran and McDonald dictionary of sentiment words specific to financial reports
unnest_tokens preserves custom attributes of data frames and data.tables
- Updated DESCRIPTION to require purrr >= 0.1.1.
cast_dtm, and other sparse casters to ignore groups in the input (#19)
unnest_tokens so that it no longer uses tidyr’s unnest, but rather a custom version that removes some overhead. In some experiments, this sped up unnest_tokens on large inputs by about 40%. This also moves tidyr from Imports to Suggests for now.
unnest_tokens now checks that there are no list columns in the input, and raises an error if present (since those cannot be unnested).
- Added a
format argument to unnest_tokens so that it can process html, xml, latex or man pages using the hunspell package, though only when
token = "words".
- Added a
get_sentiments function that takes the name of a lexicon (“nrc”, “bing”, or “sentiment”) and returns just that sentiment data frame (#25)
- Added documentation for n-grams, skip n-grams, and regex
- Added codecov and appveyor
- Added tidiers for LDA objects from topicmodels and a vignette on topic modeling
- Added function to calculate tf-idf of a tidy text dataset and a tf-idf vignette
- Fixed a bug when tidying by line/sentence/paragraph/regex and there are multiple non-text columns
- Fixed a bug when unnesting using n-grams and skip n-grams (entire text was not being collapsed)
- Added ability to pass a (custom tokenizing) function to token. Also added a collapse argument that makes the choice whether to combine lines before tokenizing explicit.
- Changed tidy.dictionary to return a tbl_df rather than a data.frame
cast_sparse to work with dplyr 0.5.0
- Deprecated the
pair_count function, which has been moved to
pairwise_count in the widyr package. This will be removed entirely in a future version.
- Initial release for text mining using tidy tools