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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fast and accurate sentiment classification using an enhanced Naive Bayes model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1305.6143/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1305.6143/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fast and accurate sentiment classification using an enhanced Naive Bayes model — mentioned by namecast</title><link>https://news.ycombinator.com/item?id=9080829</link><guid isPermaLink="false">urn:hackerpapers:mention:1305.6143:9080829</guid><pubDate>Fri, 20 Feb 2015 15:38:38 GMT</pubDate><description>&lt;p&gt;I found this paper useful for a side project I worked on a few months ago, one that made use of n-grams in a naive bayesian classifier: http://arxiv.org/pdf/1305.6143v2.pdf and the lead authors&amp;#x27;s github repos are: https://github.com/vivekn/sentiment https://github.com/vivekn/sentiment-web He&amp;#x27;s implemented &amp;#x27;negative bi-gram detection&amp;#x27; (my phrasing, not his) with this function: https://github.com/vivekn/sentiment/blob/master/info.py#L26-... ...which I found useful as a jumping off point. Good luck!&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1305.6143/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
