DEVELOPMENT... OpenML
Data
OnlineNewsPopularity

OnlineNewsPopularity

active ARFF Publicly available Visibility: public Uploaded 17-02-2016 by unknown
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Kelwin Fernandes (INESC TEC, Universidade doPorto), Pedro Vinagre (ALGORITMI Research Centre, Universidade do Minho), Paulo Cortez - ALGORITMI Research Centre (Universidade do Minho), Pedro Sernadela (Universidade de Aveiro) Source: UCI Please cite: K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal. This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity). * The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls. * Acquisition date: January 8, 2015 * The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set. Attribute Information: Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) Attribute Information: 0. url: URL of the article (non-predictive) 1. timedelta: Days between the article publication and the dataset acquisition (non-predictive) 2. n_tokens_title: Number of words in the title 3. n_tokens_content: Number of words in the content 4. n_unique_tokens: Rate of unique words in the content 5. n_non_stop_words: Rate of non-stop words in the content 6. n_non_stop_unique_tokens: Rate of unique non-stop words in the content 7. num_hrefs: Number of links 8. num_self_hrefs: Number of links to other articles published by Mashable 9. num_imgs: Number of images 10. num_videos: Number of videos 11. average_token_length: Average length of the words in the content 12. num_keywords: Number of keywords in the metadata 13. data_channel_is_lifestyle: Is data channel 'Lifestyle'? 14. data_channel_is_entertainment: Is data channel 'Entertainment'? 15. data_channel_is_bus: Is data channel 'Business'? 16. data_channel_is_socmed: Is data channel 'Social Media'? 17. data_channel_is_tech: Is data channel 'Tech'? 18. data_channel_is_world: Is data channel 'World'? 19. kw_min_min: Worst keyword (min. shares) 20. kw_max_min: Worst keyword (max. shares) 21. kw_avg_min: Worst keyword (avg. shares) 22. kw_min_max: Best keyword (min. shares) 23. kw_max_max: Best keyword (max. shares) 24. kw_avg_max: Best keyword (avg. shares) 25. kw_min_avg: Avg. keyword (min. shares) 26. kw_max_avg: Avg. keyword (max. shares) 27. kw_avg_avg: Avg. keyword (avg. shares) 28. self_reference_min_shares: Min. shares of referenced articles in Mashable 29. self_reference_max_shares: Max. shares of referenced articles in Mashable 30. self_reference_avg_sharess: Avg. shares of referenced articles in Mashable 31. weekday_is_monday: Was the article published on a Monday? 32. weekday_is_tuesday: Was the article published on a Tuesday? 33. weekday_is_wednesday: Was the article published on a Wednesday? 34. weekday_is_thursday: Was the article published on a Thursday? 35. weekday_is_friday: Was the article published on a Friday? 36. weekday_is_saturday: Was the article published on a Saturday? 37. weekday_is_sunday: Was the article published on a Sunday? 38. is_weekend: Was the article published on the weekend? 39. LDA_00: Closeness to LDA topic 0 40. LDA_01: Closeness to LDA topic 1 41. LDA_02: Closeness to LDA topic 2 42. LDA_03: Closeness to LDA topic 3 43. LDA_04: Closeness to LDA topic 4 44. global_subjectivity: Text subjectivity 45. global_sentiment_polarity: Text sentiment polarity 46. global_rate_positive_words: Rate of positive words in the content 47. global_rate_negative_words: Rate of negative words in the content 48. rate_positive_words: Rate of positive words among non-neutral tokens 49. rate_negative_words: Rate of negative words among non-neutral tokens 50. avg_positive_polarity: Avg. polarity of positive words 51. min_positive_polarity: Min. polarity of positive words 52. max_positive_polarity: Max. polarity of positive words 53. avg_negative_polarity: Avg. polarity of negative words 54. min_negative_polarity: Min. polarity of negative words 55. max_negative_polarity: Max. polarity of negative words 56. title_subjectivity: Title subjectivity 57. title_sentiment_polarity: Title polarity 58. abs_title_subjectivity: Absolute subjectivity level 59. abs_title_sentiment_polarity: Absolute polarity level 60. shares: Number of shares (target) Relevant Papers: K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal. Citation Request: K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal.

61 features

shares (target)numeric1454 unique values
0 missing
weekday_is_mondaynumeric2 unique values
0 missing
self_reference_avg_sharessnumeric8626 unique values
0 missing
weekday_is_tuesdaynumeric2 unique values
0 missing
weekday_is_wednesdaynumeric2 unique values
0 missing
weekday_is_thursdaynumeric2 unique values
0 missing
weekday_is_fridaynumeric2 unique values
0 missing
weekday_is_saturdaynumeric2 unique values
0 missing
weekday_is_sundaynumeric2 unique values
0 missing
is_weekendnumeric2 unique values
0 missing
LDA_00numeric39337 unique values
0 missing
LDA_01numeric39098 unique values
0 missing
LDA_02numeric39525 unique values
0 missing
LDA_03numeric38963 unique values
0 missing
LDA_04numeric39370 unique values
0 missing
global_subjectivitynumeric34501 unique values
0 missing
global_sentiment_polaritynumeric34695 unique values
0 missing
global_rate_positive_wordsnumeric13159 unique values
0 missing
global_rate_negative_wordsnumeric10271 unique values
0 missing
rate_positive_wordsnumeric2284 unique values
0 missing
rate_negative_wordsnumeric2284 unique values
0 missing
avg_positive_polaritynumeric27301 unique values
0 missing
min_positive_polaritynumeric33 unique values
0 missing
max_positive_polaritynumeric38 unique values
0 missing
avg_negative_polaritynumeric13841 unique values
0 missing
min_negative_polaritynumeric54 unique values
0 missing
max_negative_polaritynumeric49 unique values
0 missing
title_subjectivitynumeric673 unique values
0 missing
title_sentiment_polaritynumeric813 unique values
0 missing
abs_title_subjectivitynumeric532 unique values
0 missing
abs_title_sentiment_polaritynumeric653 unique values
0 missing
data_channel_is_busnumeric2 unique values
0 missing
timedeltanumeric724 unique values
0 missing
n_tokens_titlenumeric20 unique values
0 missing
n_tokens_contentnumeric2406 unique values
0 missing
n_unique_tokensnumeric27281 unique values
0 missing
n_non_stop_wordsnumeric1451 unique values
0 missing
n_non_stop_unique_tokensnumeric22930 unique values
0 missing
num_hrefsnumeric133 unique values
0 missing
num_self_hrefsnumeric59 unique values
0 missing
num_imgsnumeric91 unique values
0 missing
num_videosnumeric53 unique values
0 missing
average_token_lengthnumeric30136 unique values
0 missing
num_keywordsnumeric10 unique values
0 missing
data_channel_is_lifestylenumeric2 unique values
0 missing
data_channel_is_entertainmentnumeric2 unique values
0 missing
urlnominal39644 unique values
0 missing
data_channel_is_socmednumeric2 unique values
0 missing
data_channel_is_technumeric2 unique values
0 missing
data_channel_is_worldnumeric2 unique values
0 missing
kw_min_minnumeric26 unique values
0 missing
kw_max_minnumeric1076 unique values
0 missing
kw_avg_minnumeric17003 unique values
0 missing
kw_min_maxnumeric1021 unique values
0 missing
kw_max_maxnumeric35 unique values
0 missing
kw_avg_maxnumeric30834 unique values
0 missing
kw_min_avgnumeric15982 unique values
0 missing
kw_max_avgnumeric19438 unique values
0 missing
kw_avg_avgnumeric39300 unique values
0 missing
self_reference_min_sharesnumeric1255 unique values
0 missing
self_reference_max_sharesnumeric1137 unique values
0 missing
Show all 61 features

107 properties

NumberOfInstances
39644
Number of instances (rows) of the dataset.
NumberOfFeatures
61
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
60
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
-4072.46
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
39560.29
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
752324.07
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
39644
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
198.79
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
214502.13
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
2111.1
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
17694.95
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
39644
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
14.09
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
8633.94
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.28
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.52
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
39644
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-4.58
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
98.36
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
1.64
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.63
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.15
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.34
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.24
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.31
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.31
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.66
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.38
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
32.76
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
22.3
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
4.01
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
55.06
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

12 tasks

Supervised Regression on OnlineNewsPopularity
0 runs - estimation_procedure: 33% Holdout set - target_feature: shares
Clustering on OnlineNewsPopularity
0 runs
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Clustering on OnlineNewsPopularity
0 runs - estimation_procedure: 50 times Clustering
Define a new task