Geometric and Statistical Analysis of Emotions and Topics in Corpora

NLP techniques can enrich unstructured textual data, detecting topics of interest and emotions. The task of understanding emotional similarities between different topics is crucial, for example, in analyzing the Social TV landscape. A measure of how much two audiences share the same feelings is requ...

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Autores principales:	Francesco Tarasconi, Vittorio Di Tomaso
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/92d28b0390094afcbda4726257a5d2d3
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id	oai:doaj.org-article:92d28b0390094afcbda4726257a5d2d3
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spelling	oai:doaj.org-article:92d28b0390094afcbda4726257a5d2d32021-12-02T09:52:21ZGeometric and Statistical Analysis of Emotions and Topics in Corpora2499-455310.4000/ijcol.323https://doaj.org/article/92d28b0390094afcbda4726257a5d2d32015-12-01T00:00:00Zhttp://journals.openedition.org/ijcol/323https://doaj.org/toc/2499-4553NLP techniques can enrich unstructured textual data, detecting topics of interest and emotions. The task of understanding emotional similarities between different topics is crucial, for example, in analyzing the Social TV landscape. A measure of how much two audiences share the same feelings is required, but also a sound and compact representation of these similarities. After evaluating different multivariate approaches, we achieved these goals by applying Multiple Correspondence Analysis (MCA) techniques to our data. In this paper we provide background information and methodological reasons to our choice. MCA is especially suitable to analyze categorical data and detect the main contrasts among them: NLP-annotated data can be transformed and adapted to this framework. We briefly introduce the semantic annotation pipeline used in our study and provide examples of Social TV analysis, performed on Twitter data collected between October 2013 and February 2014. The benefits of examining emotions shared in social media using multivariate statistical techniques are highlighted: using additional dimensions, instead of "simple" polarity of documents, allows to detect more subtle differences in the reactions to certain shows.Francesco TarasconiVittorio Di TomasoAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 1, Iss 1, Pp 91-103 (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 Francesco Tarasconi Vittorio Di Tomaso Geometric and Statistical Analysis of Emotions and Topics in Corpora
description	NLP techniques can enrich unstructured textual data, detecting topics of interest and emotions. The task of understanding emotional similarities between different topics is crucial, for example, in analyzing the Social TV landscape. A measure of how much two audiences share the same feelings is required, but also a sound and compact representation of these similarities. After evaluating different multivariate approaches, we achieved these goals by applying Multiple Correspondence Analysis (MCA) techniques to our data. In this paper we provide background information and methodological reasons to our choice. MCA is especially suitable to analyze categorical data and detect the main contrasts among them: NLP-annotated data can be transformed and adapted to this framework. We briefly introduce the semantic annotation pipeline used in our study and provide examples of Social TV analysis, performed on Twitter data collected between October 2013 and February 2014. The benefits of examining emotions shared in social media using multivariate statistical techniques are highlighted: using additional dimensions, instead of "simple" polarity of documents, allows to detect more subtle differences in the reactions to certain shows.
format	article
author	Francesco Tarasconi Vittorio Di Tomaso
author_facet	Francesco Tarasconi Vittorio Di Tomaso
author_sort	Francesco Tarasconi
title	Geometric and Statistical Analysis of Emotions and Topics in Corpora
title_short	Geometric and Statistical Analysis of Emotions and Topics in Corpora
title_full	Geometric and Statistical Analysis of Emotions and Topics in Corpora
title_fullStr	Geometric and Statistical Analysis of Emotions and Topics in Corpora
title_full_unstemmed	Geometric and Statistical Analysis of Emotions and Topics in Corpora
title_sort	geometric and statistical analysis of emotions and topics in corpora
publisher	Accademia University Press
publishDate	2015
url	https://doaj.org/article/92d28b0390094afcbda4726257a5d2d3
work_keys_str_mv	AT francescotarasconi geometricandstatisticalanalysisofemotionsandtopicsincorpora AT vittorioditomaso geometricandstatisticalanalysisofemotionsandtopicsincorpora
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Geometric and Statistical Analysis of Emotions and Topics in Corpora

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