Mining Tweet Data - Statistic and semantic information for political tweet classification - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2014

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.
Fichier principal
Vignette du fichier
SSTM_2014_2 2.pdf (343.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Licence : CC BY NC ND - Attribution - NonCommercial - NoDerivatives
Loading...

Dates and versions

lirmm-01054908 , version 1 (29-01-2020)

Licence

Attribution - NonCommercial - NoDerivatives

Identifiers

Cite

Guillaume Tisserant, Mathieu Roche, Violaine Prince. Mining Tweet Data - Statistic and semantic information for political tweet classification. KDIR 2014 - 6th International Conference on Knowledge Discovery and Information Retrieval, Oct 2014, Rome, Italy. pp.523-529, ⟨10.5220/0005170205230529⟩. ⟨lirmm-01054908⟩
282 View
101 Download

Altmetric

Share

Gmail Facebook X LinkedIn More