UCI Machine Learning Repository: BlogFeedback Data Set


This data originates from blog posts. The raw HTML documents of the blog posts were crawled and processed. The prediction task associated with the data is the prediction of the number of comments in the upcoming 24 hours. In order to simulate this situation, we choose a basetime in the past and select the blog posts that were published at most72 hours before the selected base date/time. Then, we calculateall the features of the selected blog posts from the information that was available at the basetime, therefore each instance corresponds to a blog post.

The target is the number of comments that the blog post received in the next 24 hours relative to the basetime. In the train data, the basetimes were in the years 2010 and 2011. In the test data the basetimes were in February and March 2012. This simulates the real world situtation in which training data from the past is available to predict events in the future. The train data was generated from different basetimes that may temporally overlap.

Therefore, if you simply split the train into disjoint partitions, the underlying time intervals may overlap. Therefore, the you should use the provided, temporally disjoint train and test splits in order to ensure that theevaluation is fair. 1. 50: Average, standard deviation, min, max and median of the Attributes 51. 60 for the source of the current blog post With source we mean the blog on which the post appeared. For example, myblog.


blog. org would be the source of the post myblog. blog. org/post 2010 09 10 51: Total number of comments before basetime52: Number of comments in the last 24 hours before the basetime53: Let T1 denote the datetime 48 hours before basetime, Let T2 denote the datetime 24 hours before basetime. This attribute is the number of comments in the time period between T1 and T254: Number of comments in the first 24 hours after the publication of the blog post, but before basetime55: The difference of Attribute 52 and Attribute 5356.

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60: The same features as the attributes 51. 55, but features 56. 60 refer to the number of links trackbacks, while features 51. 55 refer to the number of comments. 61: The length of time between the publication of the blog post and basetime62: The length of the blog post63. 262: The 200 bag of words features for 200 frequent words of the text of the blog post263.

269: binary indicator features 0 or 1 for the weekday Monday. Sunday of the basetime270. 276: binary indicator features 0 or 1 for the weekday Monday. Sunday of the date of publication of the blog post277: Number of parent pages: we consider a blog post P as a parent of blog post B, if B is a reply trackback to blog post P. 278.