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Conference papers
Mining Tweet Data - Statistic and semantic information for political tweet classification
Abstract : This paper deals with the quality of textual features in messages in order to classify tweets. The aim of our study is to show how improving the representation of textual data affects the performance of learning algorithms. We will first introduce our method GenDesc. It generalises less relevant words for tweet classi- fication. Secondly we compare and discuss the types of textual features given by different approaches. More precisely we discuss the semantic specificity of textual features, e.g. Named Entity, HashTag.
Cited literature [21 references]
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01054908
Contributor : Mathieu Roche <>
Submitted on : Wednesday, January 29, 2020 - 5:09:36 PM
Last modification on : Thursday, July 2, 2020 - 1:59:33 PM
Document(s) archivé(s) le : Thursday, April 30, 2020 - 6:47:55 PM
Contributor : Mathieu Roche <>
Submitted on : Wednesday, January 29, 2020 - 5:09:36 PM
Last modification on : Thursday, July 2, 2020 - 1:59:33 PM
Document(s) archivé(s) le : Thursday, April 30, 2020 - 6:47:55 PM
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SSTM_2014_2 2.pdf
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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License
