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This has been fixed (see #7).</p>\n</li>\n<li><p><code>highlight</code> parsed sentences differently than the main <code>sentiment</code> function \nresulting in an error when <code>original.text</code> was supplied that contained a colon\nor semi-colon.  Spotted by Patrick Carlson (see #2).</p>\n</li>\n</ul>\n<p><strong>MINOR FEATURES</strong></p>\n<ul>\n<li><code>as_key</code> and <code>update_key</code> now coerce the first column of the <code>x</code> argument \ndata.frame to lower case and warn if capital letters are found.</li>\n</ul>\n<p><strong>IMPROVEMENTS</strong></p>\n<ul>\n<li><p>A section on creating and updating dictionaries was added to the README:\n<a href=\"https://github.com/trinker/sentimentr#making-and-updating-dictionaries\">https://github.com/trinker/sentimentr#making-and-updating-dictionaries</a></p>\n</li>\n<li><p><code>plot.sentiment_by</code> no longer color codes by grouping variables.  This was\ndistracting and removed.  A jitter + red average sentiment + boxplot visual\nrepresentation is used.</p>\n</li>\n</ul>\n<p><strong>CHANGES</strong></p>\n<ul>\n<li>Default sentiment and valence shifters get the following additions: <ul>\n<li><code>polarity_table</code>: \"excessively\", 'overly', 'unduly', 'too much', 'too many', \n'too often', 'i wish', 'too good', 'too high', 'too tough'</li>\n<li><code>valence_shifter_table</code>: \"especially\"</li>\n</ul>\n</li>\n</ul>\nsentimentr 0.1.0 - 0.1.3\n<p><strong>BUG FIXES</strong></p>\n<ul>\n<li><p><code>get_sentences</code> converted to lower case too early in the regex parsing,\nresulting in missed sentence boundary detection.  This has been corrected.</p>\n</li>\n<li><p><code>highlight</code> failed for some occasions when using <code>original.text</code> because the\nsplitting algorithm for <code>sentiment</code> was different. <code>sentiment</code>'s split algorithm\nnow matches and is more accurate but at the cost of speed.</p>\n</li>\n</ul>\n<p><strong>NEW FEATURES</strong></p>\n<ul>\n<li><p><code>emoticons</code> dictionary added.  This is a simple dataset containing common\nemoticons (adapted from <a href=\"http://www.lingo2word.com/lists/emoticon_listH.html\">Popular Emoticon List</a>)</p>\n</li>\n<li><p><code>replace_emoticon</code> function added to replace emoticons with word equivalents.</p>\n</li>\n<li><p><code>get_sentences2</code> added to allow for users that may want to get sentences from\ntext and retain case and non-sentence boundary periods.  This should be\npreferable in such instances where these features are deemed important to the\nanalysis at hand.</p>\n</li>\n<li><p><code>highlight</code> added to allow positive/negative text highlighting.</p>\n</li>\n<li><p><code>cannon_reviews</code> data set added containing Amazon product reviews for the\nCannon G3 Camera compiled by Hu and Liu (2004).</p>\n</li>\n<li><p><code>replace_ratings</code> function + <code>ratings</code> data set added to replace ratings.</p>\n</li>\n<li><p><code>polarity_table</code> gets an upgrade with new positive and negative words to\nimprove accuracy.</p>\n</li>\n<li><p><code>valence_shifters_table</code> picks up a few non-traditional negators.  Full list\nincludes: \"could have\", \"would have\", \"should have\", \"would be\",\n\"would suggest\", \"strongly suggest\".</p>\n</li>\n<li><p><code>is_key</code> and <code>update_key</code> added to test and easily update keys.</p>\n</li>\n<li><p><code>grades</code> dictionary added.  This is a simple dataset containing common\ngrades and word equivalents.</p>\n</li>\n<li><p><code>replace_grade</code> function added to replace grades with word equivalents.</p>\n</li>\n</ul>\n<p><strong>IMPROVEMENTS</strong></p>\n<ul>\n<li><p><code>plot.sentiment</code> now uses <code>...</code> to pass parameters to <strong>syuzhet</strong>'s\n<code>get_transformed_values</code>.</p>\n</li>\n<li><p><code>as_key</code>, <code>is_key</code>, &amp; <code>update_key</code> all pick up a logical <code>sentiment</code> argument\nthat allows keys that have character y columns (2nd column).</p>\n</li>\n</ul>\nsentimentr 0.0.1\n<p>This package is designed to quickly calculate text polarity sentiment at the\nsentence level and optionally aggregate by rows or grouping variable(s).</p>",
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