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DOI: 10.4230/OASIcs.SLATE.2014.185

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

Authors: Hugo Rosa, João Paulo Carvalho, and Fernando Batista

Published in: OASIcs, Volume 38, 3rd Symposium on Languages, Applications and Technologies (2014)

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.

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Hugo Rosa, João Paulo Carvalho, and Fernando Batista. Detecting a Tweet’s Topic within a Large Number of Portuguese Twitter Trends. In 3rd Symposium on Languages, Applications and Technologies. Open Access Series in Informatics (OASIcs), Volume 38, pp. 185-199, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{rosa_et_al:OASIcs.SLATE.2014.185,
  author =	{Rosa, Hugo and Carvalho, Jo\~{a}o Paulo and Batista, Fernando},
  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 =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-68-2},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{38},
  editor =	{Pereira, Maria Jo\~{a}o Varanda and Leal, Jos\'{e} Paulo and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2014.185},
  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}
}

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

Abstract

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