French Presidential Elections: What are the most Efficient Measures for Tweets?

Flavien Bouillot 1 Pascal Poncelet 1 Mathieu Roche 2 Dino Ienco 3, 1 Elnaz Bigdeli 4 Stan Matwin 4
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Tweets exchanged over the Internet are an important source of information even if their characteristics make them dif- ficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we address the problem of extracting relevant topics through tweets coming from different commu- nities. More precisely we are interested to address the fol- lowing question: which are the most relevant terms given a community. To answer this question we define and evaluate new variants of the traditional TF-IDF. Furthermore we also show that our measures are well suited to recommend a community affiliation to a new user. Experiments have been conducted on tweets collected during French Presiden- tial and Legislative elections in 2012. The results underline the quality and the usefulness of our proposal.
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Flavien Bouillot, Pascal Poncelet, Mathieu Roche, Dino Ienco, Elnaz Bigdeli, et al.. French Presidential Elections: What are the most Efficient Measures for Tweets?. PLEAD: Proceedings of the Workshop Politics, Elections and Data, 2012, Maui, United States. pp.23-30, ⟨10.1145/2389661.2389669⟩. ⟨lirmm-00801028⟩

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