Learning Affect with Distributional Semantic Models

The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical aff...

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Autores principales:	Lucia C. Passaro, Alessandro Bondielli, Alessandro Lenci
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2017
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/8a580bfbfc934ab69346ce5a50c109d5
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id	oai:doaj.org-article:8a580bfbfc934ab69346ce5a50c109d5
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spelling	oai:doaj.org-article:8a580bfbfc934ab69346ce5a50c109d52021-12-02T09:52:18ZLearning Affect with Distributional Semantic Models2499-455310.4000/ijcol.550https://doaj.org/article/8a580bfbfc934ab69346ce5a50c109d52017-12-01T00:00:00Zhttp://journals.openedition.org/ijcol/550https://doaj.org/toc/2499-4553The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data.Lucia C. PassaroAlessandro BondielliAlessandro LenciAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 3, Iss 2, Pp 23-36 (2017)
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 Lucia C. Passaro Alessandro Bondielli Alessandro Lenci Learning Affect with Distributional Semantic Models
description	The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data.
format	article
author	Lucia C. Passaro Alessandro Bondielli Alessandro Lenci
author_facet	Lucia C. Passaro Alessandro Bondielli Alessandro Lenci
author_sort	Lucia C. Passaro
title	Learning Affect with Distributional Semantic Models
title_short	Learning Affect with Distributional Semantic Models
title_full	Learning Affect with Distributional Semantic Models
title_fullStr	Learning Affect with Distributional Semantic Models
title_full_unstemmed	Learning Affect with Distributional Semantic Models
title_sort	learning affect with distributional semantic models
publisher	Accademia University Press
publishDate	2017
url	https://doaj.org/article/8a580bfbfc934ab69346ce5a50c109d5
work_keys_str_mv	AT luciacpassaro learningaffectwithdistributionalsemanticmodels AT alessandrobondielli learningaffectwithdistributionalsemanticmodels AT alessandrolenci learningaffectwithdistributionalsemanticmodels
_version_	1718397956974968832

Learning Affect with Distributional Semantic Models

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