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

Guillaume Tisserant; Mathieu Roche; Violaine Prince

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Paper

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, 523-529, 2014 , Rome, Italy

Authors: Guillaume Tisserant ¹ ; Mathieu Roche ² and Violaine Prince ¹

Affiliations: ¹ Université Montpellier 2, France ; ² Université Montpellier 2 and AgroParisTech, France

Keyword(s): Text Mining, Classification, Tweets.

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.

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Paper citation in several formats:

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 (IC3K 2014) - SSTM; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 523-529. DOI: 10.5220/0005170205230529

@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 (IC3K 2014) - SSTM},
year={2014},
pages={523-529},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005170205230529},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - SSTM
TI - Mining Tweet Data - Statistic and Semantic Information for Political Tweet Classification
SN - 978-989-758-048-2
IS - 2184-3228
AU - Tisserant, G.
AU - Roche, M.
AU - Prince, V.
PY - 2014
SP - 523
EP - 529
DO - 10.5220/0005170205230529
PB - SciTePress