Predict keywords activities in a online social media
Donated on 12/11/2013
The data from Twitter was collected during 360 consecutive days. It was done by querying 1497 English keywords sampled from Wikipedia. This dataset is proposed in a Learning to rank setting.
Dataset Characteristics
Multivariate, Sequential, Time-Series
Subject Area
Computer Science
Associated Tasks
-
Feature Type
Integer, Real
# Instances
51
# Features
35
Dataset Information
Additional Information
See files and/or http://ama.liglab.fr/resourcestools/datasets/predict-keywords-activities-in-a-online-social-media/
Has Missing Values?
No
Variable Information
See files and/or http://ama.liglab.fr/resourcestools/datasets/predict-keywords-activities-in-a-online-social-media/
Kawala,Franois, Douzal,Ahlame, Gaussier,Eric, and Diemert,Eustache. (2013). Predict keywords activities in a online social media. UCI Machine Learning Repository. https://doi.org/10.24432/C5W31C.
@misc{misc_predict_keywords_activities_in_a_online_social_media_276,
author = {Kawala,Franois, Douzal,Ahlame, Gaussier,Eric, and Diemert,Eustache},
title = {{Predict keywords activities in a online social media}},
year = {2013},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5W31C}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset predict_keywords_activities_in_a_online_social_media = fetch_ucirepo(id=276) # data (as pandas dataframes) X = predict_keywords_activities_in_a_online_social_media.data.features y = predict_keywords_activities_in_a_online_social_media.data.targets # metadata print(predict_keywords_activities_in_a_online_social_media.metadata) # variable information print(predict_keywords_activities_in_a_online_social_media.variables)
Creators
Franois Kawala
Ahlame Douzal
Eric Gaussier
Eustache Diemert
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.