A vector space model for automatic indexing
Communications of the ACM
Contextualizing data warehouses with documents
Decision Support Systems
Text Cube: Computing IR Measures for Multidimensional Text Database Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
French presidential elections: what are the most efficient measures for tweets?
Proceedings of the first edition workshop on Politics, elections and data
OLAPing social media: the case of Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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Tweets exchanged over the Internet represent an important source of information, even if their characteristics make them difficult to analyze (a maximum of 140 characters, etc.). In this paper, we define a data warehouse model to analyze large volumes of tweets by proposing measures relevant in the context of knowledge discovery. The use of data warehouses as a tool for the storage and analysis of textual documents is not new but current measures are not well-suited to the specificities of the manipulated data. We also propose a new way for extracting the context of a concept in a hierarchy. Experiments carried out on real data underline the relevance of our proposal.