% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{news}
\alias{news}
\title{Social media news sharing}
\format{
A data frame with 39,644 rows and 54 columns:
\describe{
\item{n_tokens_title}{Number of words in the title}
\item{n_tokens_content}{Number of words in the content}
\item{n_unique_tokens}{Rate of unique words in the content}
\item{num_hrefs}{Number of links}
\item{num_self_hrefs}{Number of links to other articles published by Mashable}
\item{num_imgs}{Number of images}
\item{num_videos}{Number of videos}
\item{average_token_length}{Average length of the words in the content}
\item{num_keywords}{Number of keywords in the metadata}
\item{data_channel_is_lifestyle}{Is data channel 'Lifestyle'?}
\item{data_channel_is_entertainment}{Is data channel 'Entertainment'?}
\item{data_channel_is_bus}{Is data channel 'Business'?}
\item{data_channel_is_socmed}{Is data channel 'Social Media'?}
\item{data_channel_is_tech}{Is data channel 'Tech'?}
\item{data_channel_is_world}{Is data channel 'World'?}
\item{kw_min_min}{Worst keyword (min. shares)}
\item{kw_max_min}{Worst keyword (max. shares)}
\item{kw_avg_min}{Worst keyword (avg. shares)}
\item{kw_max_max}{Best keyword (max. shares)}
\item{kw_avg_max}{Best keyword (avg. shares)}
\item{kw_min_avg}{Avg. keyword (min. shares)}
\item{kw_max_avg}{Avg. keyword (max. shares)}
\item{kw_avg_avg}{Avg. keyword (avg. shares)}
\item{self_reference_min_shares}{Min. shares of referenced articles in Mashable}
\item{self_reference_avg_sharess}{Avg. shares of referenced articles in Mashable}
\item{weekday_is_monday}{Was the article published on a Monday?}
\item{weekday_is_tuesday}{Was the article published on a Tuesday?}
\item{weekday_is_wednesday}{Was the article published on a Wednesday?}
\item{weekday_is_thursday}{Was the article published on a Thursday?}
\item{weekday_is_friday}{Was the article published on a Friday?}
\item{weekday_is_saturday}{Was the article published on a Saturday?}
\item{weekday_is_sunday}{Was the article published on a Sunday?}
\item{LDA_00}{Closeness to LDA topic 0}
\item{LDA_01}{Closeness to LDA topic 1}
\item{LDA_02}{Closeness to LDA topic 2}
\item{LDA_03}{Closeness to LDA topic 3}
\item{LDA_04}{Closeness to LDA topic 4}
\item{global_subjectivity}{Text subjectivity}
\item{global_sentiment_polarity}{Text sentiment polarity}
\item{global_rate_positive_words}{Rate of positive words in the content}
\item{global_rate_negative_words}{Rate of negative words in the content}
\item{rate_positive_words}{Rate of positive words among non-neutral tokens}
\item{rate_negative_words}{Rate of negative words among non-neutral tokens}
\item{avg_positive_polarity}{Avg. polarity of positive words}
\item{min_positive_polarity}{Min. polarity of positive words}
\item{max_positive_polarity}{Max. polarity of positive words}
\item{avg_negative_polarity}{Avg. polarity of negative  words}
\item{min_negative_polarity}{Min. polarity of negative  words}
\item{max_negative_polarity}{Max. polarity of negative  words}
\item{title_subjectivity}{Title subjectivity}
\item{title_sentiment_polarity}{Title polarity}
\item{abs_title_subjectivity}{Absolute subjectivity level}
\item{abs_title_sentiment_polarity}{Absolute polarity level}
\item{shares}{Number of shares (target)}
}
}
\source{
\url{https://archive.ics.uci.edu/dataset/332/online+news+popularity}
}
\usage{
news
}
\description{
A dataset containing features about articles published by Mashable in a period
of two years from January 7, 2013, to January 7, 2015. The purpose of collecting
the data was to predict the number of shares of the news articles on social networks.
Compared to the source, seven features are excluded. Two (url and a timestamp)
are not relevant predictors and five are redundant or almost redundant, leading
to collinearity.
}
\keyword{datasets}
