Graph-based Techniques for Topic Classification of Tweets in Spanish

Cordobés de la Calle, Héctor; Fernández Anta, Antonio; Chiroque, Luis F.; Pérez, Fernando; Redondo, Teófilo; Santos, Agustín

doi:DOI:10.9781/ijimai.2014.254

dc.contributor.author	Cordobés de la Calle, Héctor
dc.contributor.author	Fernández Anta, Antonio
dc.contributor.author	Chiroque, Luis F.
dc.contributor.author	Pérez, Fernando
dc.contributor.author	Redondo, Teófilo
dc.contributor.author	Santos, Agustín
dc.date.accessioned	2021-07-13T10:08:52Z
dc.date.available	2021-07-13T10:08:52Z
dc.date.issued	2014-03
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dc.identifier.issn	ISSN 1989 - 1660
dc.identifier.uri	http://hdl.handle.net/20.500.12761/1287
dc.description.abstract	Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy.
dc.language.iso	eng
dc.publisher	IMAI Research Group
dc.title	Graph-based Techniques for Topic Classification of Tweets in Spanish	en
dc.type	journal article
dc.journal.title	IJIMAI International Journal of Interactive Multimedia and Artificial Intelligence (Special issue: AI Techniques to Evaluate Economics and Happiness)
dc.type.hasVersion	VoR
dc.rights.accessRights	open access
dc.volume.number	2
dc.issue.number	5
dc.identifier.doi	DOI:10.9781/ijimai.2014.254
dc.page.final	37
dc.page.initial	31
dc.subject.keyword	Classification
dc.subject.keyword	Graphs
dc.subject.keyword	Happiness
dc.subject.keyword	NLP
dc.subject.keyword	Text Classification
dc.subject.keyword	Topic Classification
dc.description.status	pub
dc.eprint.id	http://eprints.networks.imdea.org/id/eprint/723

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