<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>crystalis</author><text>To be fair, several posters can earn their points on name alone:&lt;p&gt;- grellas can post a one liner on a law issue&lt;p&gt;- DarkShikari can post about video codecs&lt;p&gt;- tptacek can post about security&lt;p&gt;- patio11 can post about bingo&lt;p&gt;- edw519 can post a grocery list</text><parent_chain><item><author>zefhous</author><text>I&apos;d love to see how well it could predict comment ratings from Hacker News.&lt;p&gt;The following data would be a good start:&lt;p&gt;1. Text of comment&lt;p&gt;2. How many points the comment has&lt;p&gt;3. How many points the article has&lt;p&gt;4. Time article was posted&lt;p&gt;5. Time comment was posted&lt;p&gt;I&apos;d also be interested to see what kind of user bias there is. If you don&apos;t provide user names, you could see what kind of rating a comment &lt;i&gt;should&lt;/i&gt; have based on its content, and what rating it actually has because certain users are generally loved (pg) or hated (jasonmcalacanis) by the community.</text></item></parent_chain></comment><story><title>Google Prediction API</title><url>http://code.google.com/apis/predict/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>physcab</author><text>That would be interesting.  Does anyone have a dataset?  Ideally to do this type analysis you would also need to know the upvote (or downvote) history of the users so you can pinpoint any bias, or predict which comments &lt;i&gt;you as a user&lt;/i&gt; are most fond of reading.</text><parent_chain><item><author>zefhous</author><text>I&apos;d love to see how well it could predict comment ratings from Hacker News.&lt;p&gt;The following data would be a good start:&lt;p&gt;1. Text of comment&lt;p&gt;2. How many points the comment has&lt;p&gt;3. How many points the article has&lt;p&gt;4. Time article was posted&lt;p&gt;5. Time comment was posted&lt;p&gt;I&apos;d also be interested to see what kind of user bias there is. If you don&apos;t provide user names, you could see what kind of rating a comment &lt;i&gt;should&lt;/i&gt; have based on its content, and what rating it actually has because certain users are generally loved (pg) or hated (jasonmcalacanis) by the community.</text></item></parent_chain></comment><story><title>Google Prediction API</title><url>http://code.google.com/apis/predict/</url></story>