License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SLATE.2014.185
URN: urn:nbn:de:0030-drops-45696
URL: https://drops.dagstuhl.de/opus/volltexte/2014/4569/
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Rosa, Hugo ; Carvalho, João Paulo ; Batista, Fernando

Detecting a Tweet’s Topic within a Large Number of Portuguese Twitter Trends

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Abstract

In this paper we propose to approach the subject of Twitter Topic Detection when in the presence of a large number of trending topics. We use a new technique, called Twitter Topic Fuzzy Fingerprints, and compare it with two popular text classification techniques, Support Vector Machines (SVM) and k-Nearest Neighbours (kNN). Preliminary results show that it outperforms the other two techniques, while still being much faster, which is an essential feature when processing large volumes of streaming data. We focused on a data set of Portuguese language tweets and the respective top trends as indicated by Twitter.

BibTeX - Entry

@InProceedings{rosa_et_al:OASIcs:2014:4569,
  author =	{Hugo Rosa and Jo{\~a}o Paulo Carvalho and Fernando Batista},
  title =	{{Detecting a Tweet’s Topic within a Large Number of Portuguese Twitter Trends}},
  booktitle =	{3rd Symposium on Languages, Applications and Technologies},
  pages =	{185--199},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-68-2},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{38},
  editor =	{Maria Jo{\~a}o Varanda Pereira and Jos{\'e} Paulo Leal and Alberto Sim{\~o}es},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2014/4569},
  URN =		{urn:nbn:de:0030-drops-45696},
  doi =		{10.4230/OASIcs.SLATE.2014.185},
  annote =	{Keywords: topic detection, social networks data mining, Twitter, Portuguese language}
}

Keywords: topic detection, social networks data mining, Twitter, Portuguese language
Collection: 3rd Symposium on Languages, Applications and Technologies
Issue Date: 2014
Date of publication: 18.06.2014


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