Mining Tweet Data - Statistic and Semantic Information for Political Tweet Classification

Guillaume Tisserant, Mathieu Roche, Violaine Prince

2014

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 YYYYY. It generalises less relevant words for tweet classification. 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.

Paper Citation

in Harvard Style

Tisserant G., Roche M. and Prince V. (2014). Mining Tweet Data - Statistic and Semantic Information for Political Tweet Classification . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014) ISBN 978-989-758-048-2, pages 523-529. DOI: 10.5220/0005170205230529

in Bibtex Style

@conference{sstm14,
author={Guillaume Tisserant and Mathieu Roche and Violaine Prince},
title={Mining Tweet Data - Statistic and Semantic Information for Political Tweet Classification},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)},
year={2014},
pages={523-529},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005170205230529},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)
TI - Mining Tweet Data - Statistic and Semantic Information for Political Tweet Classification
SN - 978-989-758-048-2
AU - Tisserant G.
AU - Roche M.
AU - Prince V.
PY - 2014
SP - 523
EP - 529
DO - 10.5220/0005170205230529