trinker/sentimentr: version 0.4.0

NEWS Versioning

Releases will be numbered with the following semantic versioning format:

<major>.<minor>.<patch>

And constructed with the following guidelines:

• Breaking backward compatibility bumps the major (and resets the minor and patch)
• New additions without breaking backward compatibility bumps the minor (and resets the patch)
• Bug fixes and misc changes bumps the patch

sentimentr 0.3.0 -

BUG FIXES

• Missing documentation for `but' conjunctions added to the documentation.
  Spotted by Richard Watson (see #23).

NEW FEATURES

• extract_sentiment_terms added to enable users to extract the sentiment terms from text as polarity would return in the qdap package.

MINOR FEATURES

• update_polarity_table and update_valence_shifter_table added to abstract away thinking about the comparison argument to update_key.

IMPROVEMENTS

CHANGES

sentimentr 0.2.0 - 0.2.3

BUG FIXES

• Commas were not handled properly in some cases. This has been fixed (see #7).

• highlight parsed sentences differently than the main sentiment function resulting in an error when original.text was supplied that contained a colon or semi-colon. Spotted by Patrick Carlson (see #2).

MINOR FEATURES

• as_key and update_key now coerce the first column of the x argument data.frame to lower case and warn if capital letters are found.

IMPROVEMENTS

• A section on creating and updating dictionaries was added to the README: https://github.com/trinker/sentimentr#making-and-updating-dictionaries

• plot.sentiment_by no longer color codes by grouping variables. This was distracting and removed. A jitter + red average sentiment + boxplot visual representation is used.

CHANGES

• Default sentiment and valence shifters get the following additions:
  • polarity_table: "excessively", 'overly', 'unduly', 'too much', 'too many', 'too often', 'i wish', 'too good', 'too high', 'too tough'
  • valence_shifter_table: "especially"

sentimentr 0.1.0 - 0.1.3

BUG FIXES

• get_sentences converted to lower case too early in the regex parsing, resulting in missed sentence boundary detection. This has been corrected.

• highlight failed for some occasions when using original.text because the splitting algorithm for sentiment was different. sentiment's split algorithm now matches and is more accurate but at the cost of speed.

NEW FEATURES

• emoticons dictionary added. This is a simple dataset containing common emoticons (adapted from Popular Emoticon List)

• replace_emoticon function added to replace emoticons with word equivalents.

• get_sentences2 added to allow for users that may want to get sentences from text and retain case and non-sentence boundary periods. This should be preferable in such instances where these features are deemed important to the analysis at hand.

• highlight added to allow positive/negative text highlighting.

• cannon_reviews data set added containing Amazon product reviews for the Cannon G3 Camera compiled by Hu and Liu (2004).

• replace_ratings function + ratings data set added to replace ratings.

• polarity_table gets an upgrade with new positive and negative words to improve accuracy.

• valence_shifters_table picks up a few non-traditional negators. Full list includes: "could have", "would have", "should have", "would be", "would suggest", "strongly suggest".

• is_key and update_key added to test and easily update keys.

• grades dictionary added. This is a simple dataset containing common grades and word equivalents.

• replace_grade function added to replace grades with word equivalents.

IMPROVEMENTS

• plot.sentiment now uses ... to pass parameters to syuzhet's get_transformed_values.

• as_key, is_key, & update_key all pick up a logical sentiment argument that allows keys that have character y columns (2nd column).

sentimentr 0.0.1

This package is designed to quickly calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).