
## Source
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)

## Data Set Information:

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 most
72 hours before the selected base date/time. Then, we calculate
all 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 the
evaluation is fair.

## Attribute Information:

1...50:Average, standard deviation, min, max and median of them 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 basetime

52: Number of comments in the last 24 hours before the
basetime

53: 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 T2

54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime

55: The difference of Attribute 52 and Attribute 53

56...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 basetime

62: The length of the blog post

63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post

263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime

270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post

277: 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...280:
Minimum, maximum, average number of comments that the
parents received

281: The target: the number of comments in the next 24 hours
(relative to basetime)

task_categories:tabular-regression task_categories:tabular-classification size_categories:10K<n<100K tabular region:us
{"size_categories": ["10K<n<100K"], "task_categories": ["tabular-regression", "tabular-classification"], "tags": ["tabular"]}
TAGS
#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us 


## Source
Source: UCI

## Data Set Information:

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 most
72 hours before the selected base date/time. Then, we calculate
all 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 the
evaluation is fair.

## Attribute Information:

1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, URL would be the source of the post URL

51: Total number of comments before basetime

52: Number of comments in the last 24 hours before the
basetime

53: 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 T2

54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime

55: The difference of Attribute 52 and Attribute 53

56...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 basetime

62: The length of the blog post

63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post

263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime

270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post

277: 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...280:
Minimum, maximum, average number of comments that the
parents received

281: The target: the number of comments in the next 24 hours
(relative to basetime)

## Source
Source: UCI ## Data Set Information:

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 most
72 hours before the selected base date/time. Then, we calculate
all 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 the
evaluation is fair. ## Attribute Information:

1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, URL would be the source of the post URL

51: Total number of comments before basetime

52: Number of comments in the last 24 hours before the
basetime

53: 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 T2

54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime

55: The difference of Attribute 52 and Attribute 53

56...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 basetime

62: The length of the blog post

63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post

263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime

270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post

277: 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...280:
Minimum, maximum, average number of comments that the
parents received

281: The target: the number of comments in the next 24 hours
(relative to basetime)
TAGS
#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us 
 ## Source
Source: UCI ## Data Set Information:

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 most
72 hours before the selected base date/time. Then, we calculate
all 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 the
evaluation is fair. ## Attribute Information:

1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, URL would be the source of the post URL

51: Total number of comments before basetime

52: Number of comments in the last 24 hours before the
basetime

53: 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 T2

54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime

55: The difference of Attribute 52 and Attribute 53

56...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 basetime

62: The length of the blog post

63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post

263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime

270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post

277: 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...280:
Minimum, maximum, average number of comments that the
parents received

281: The target: the number of comments in the next 24 hours
(relative to basetime)
passage: TAGS
#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us 
## Source
Source: UCI## Data Set Information:

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 most
72 hours before the selected base date/time. Then, we calculate
all 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 the
evaluation is fair.