# tidytext 0.2.5

* `reorder_within()` now handles multiple variables, thanks to @tmastny (#170)
* Move stopwords to Suggests so tidytext can be installed on older versions of R
* Pass `to_lower` argument to other tokenizing functions, for more consistent behavior (#175)
* Add `glance()` method for stm's estimated regressions, thanks to @vincentarelbundock (#176)

# tidytext 0.2.4

* Update tidying test for new tibble release (inner names for columns)
* Deprecate SE versions of main functions (have long been replaced by tidy eval semantics)
* Improve error handling throughout

# tidytext 0.2.3

* Wrapper tokenization functions for n-grams, characters, sentences, tweets, and more, thanks to @ColinFay (#137).
* Simplify get_sentiments() thanks to @jennybc (#151).
* Fix flaky tests for corpus tidiers.

# tidytext 0.2.2

* Access NRC lexicon via textdata package

# tidytext 0.2.1

* Fix bug in `augment()` function for stm topic model.
* Warn when tf-idf is negative, thanks to @EmilHvitfeldt (#112).
* Switch from importing broom to importing generics, for lighter dependencies (#133).
* Add functions for reordering factors (such as for ggplot2 bar plots) thanks to @tmastny (#110).
* Update to `tibble()` where appropriate, thanks to @luisdza (#136).
* Clarify documentation about impact of lowercase conversion on URLs (#139).
* Change how sentiment lexicons are accessed from package (remove NRC lexicon entirely, access AFINN and Loughran lexicons via textdata package so they are no longer included in this package).

# tidytext 0.2.0

* Improvements to documentation (#117)
* Fix for NSE thanks to @lepennec (#122).
* Tidier for estimated regressions from **stm** package thanks to @jefferickson (#115).
* Tidier for correlated topic model from **topicmodels** package (#123).

# tidytext 0.1.9

* Updates to documentation (#109) thanks to Emil Hvitfeldt.
* Add new tokenizers for tweets, Penn Treebank to `unnest_tokens()`.
* Better error message (#111) and code styling.
* Declare dependency for tests.

# tidytext 0.1.8

* 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).

# tidytext 0.1.7

* Updated more docs/tests so package can build on R-oldrel. (Still trying!)

# tidytext 0.1.6

* `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).
* Add `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.

# tidytext 0.1.5

* Change how `NA` values are handled in `unnest_tokens` so they no longer cause other columns to become `NA` (#82).
* Update tidiers and casters to align with quanteda v1.0 (#87).
* Handle input/output object classes (such as `data.table`) consistently (#88).

# tidytext 0.1.4

* Fix tidier for quanteda dictionary for correct class (#71).
* Add a [pkgdown site](https://juliasilge.github.io/tidytext).
* Convert NSE from underscored function to tidyeval (`unnest_tokens`, `bind_tf_idf`, all sparse casters) (#67, #74).
* Added tidiers for topic models from the `stm` package (#51).

# tidytext 0.1.3

* `get_sentiments` now works regardless of whether `tidytext` has been loaded or not (#50).
* `unnest_tokens` now supports data.table objects (#37).
* Fixed `to_lower` parameter in `unnest_tokens` to work properly for all tokenizing options.
* Updated `tidy.corpus`, `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 `mallet` package
* Added the Loughran and McDonald dictionary of sentiment words specific to financial reports
* `unnest_tokens` preserves custom attributes of data frames and data.tables

# tidytext 0.1.2

* Updated DESCRIPTION to require purrr >= 0.1.1.
* Fixed `cast_sparse`, `cast_dtm`, and other sparse casters to ignore groups in the input (#19)
* Changed `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)

# tidytext 0.1.1

* 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
* Updated `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](https://github.com/dgrtwo/widyr). This will be removed entirely in a future version.
 
# tidytext 0.1.0
 
* Initial release for text mining using tidy tools



