Context-aware Models for Twitter Sentiment Analysis

Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of thi...

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Autores principales:	Giuseppe Castellucci, Danilo Croce, Andrea Vanzo, Roberto Basili
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2015
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/e9e8dfa168984d86b470b3bd694aad65
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spelling	oai:doaj.org-article:e9e8dfa168984d86b470b3bd694aad652021-12-02T09:52:32ZContext-aware Models for Twitter Sentiment Analysis2499-455310.4000/ijcol.322https://doaj.org/article/e9e8dfa168984d86b470b3bd694aad652015-12-01T00:00:00Zhttp://journals.openedition.org/ijcol/322https://doaj.org/toc/2499-4553Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated for the detection of the polarity of tweet messages. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model has been here adopted to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging better embodies evidence about the contexts and is able to increase the accuracy of the resulting polarity detection process. These evidences are strengthened as experiments are successfully carried out over two different languages: Italian and English. Results are particularly interesting as the approach is flexible and does not rely on any manually coded resources.Giuseppe CastellucciDanilo CroceAndrea VanzoRoberto BasiliAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 1, Iss 1, Pp 75-89 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social Sciences H Computational linguistics. Natural language processing P98-98.5
spellingShingle	Social Sciences H Computational linguistics. Natural language processing P98-98.5 Giuseppe Castellucci Danilo Croce Andrea Vanzo Roberto Basili Context-aware Models for Twitter Sentiment Analysis
description	Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated for the detection of the polarity of tweet messages. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model has been here adopted to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging better embodies evidence about the contexts and is able to increase the accuracy of the resulting polarity detection process. These evidences are strengthened as experiments are successfully carried out over two different languages: Italian and English. Results are particularly interesting as the approach is flexible and does not rely on any manually coded resources.
format	article
author	Giuseppe Castellucci Danilo Croce Andrea Vanzo Roberto Basili
author_facet	Giuseppe Castellucci Danilo Croce Andrea Vanzo Roberto Basili
author_sort	Giuseppe Castellucci
title	Context-aware Models for Twitter Sentiment Analysis
title_short	Context-aware Models for Twitter Sentiment Analysis
title_full	Context-aware Models for Twitter Sentiment Analysis
title_fullStr	Context-aware Models for Twitter Sentiment Analysis
title_full_unstemmed	Context-aware Models for Twitter Sentiment Analysis
title_sort	context-aware models for twitter sentiment analysis
publisher	Accademia University Press
publishDate	2015
url	https://doaj.org/article/e9e8dfa168984d86b470b3bd694aad65
work_keys_str_mv	AT giuseppecastellucci contextawaremodelsfortwittersentimentanalysis AT danilocroce contextawaremodelsfortwittersentimentanalysis AT andreavanzo contextawaremodelsfortwittersentimentanalysis AT robertobasili contextawaremodelsfortwittersentimentanalysis
_version_	1718397978560954368

Context-aware Models for Twitter Sentiment Analysis

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