Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning

[EN] Over the past decade, social media platforms have undergone significant and rapid expansion. One of the key challenges has been effectively analysing the vast amount of unstructured user-generated data they produce. This research delves into the analysis of Italian Twitter data through the appl...

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Detalhes bibliográficos
Autores: Caliskan, Ufuk, Pappagallo, Angela, Ortame, Francesco, Bruno, Mauro, Pugliese, Francesco
Formato: capítulo de livro
Fecha de publicación:2024
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/208677
Acesso em linha:https://riunet.upv.es/handle/10251/208677
Access Level:acceso abierto
Palavra-chave:Politics
Deep learning
Artificial intelligence
Big data
Statistics
Sentiment
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spelling Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep LearningCaliskan, UfukPappagallo, AngelaOrtame, FrancescoBruno, MauroPugliese, FrancescoPoliticsDeep learningArtificial intelligenceBig dataStatisticsSentiment[EN] Over the past decade, social media platforms have undergone significant and rapid expansion. One of the key challenges has been effectively analysing the vast amount of unstructured user-generated data they produce. This research delves into the analysis of Italian Twitter data through the application of advanced deep learning models across three primary objectives: text classification, sentiment analysis, and hate analysis. Five cutting-edge models are evaluated, each utilizing distinct word embeddings.Furthermore, this study investigates the effects of processing emojis and emoticons in Italian tweets on sentiment and hate analysis. We compare model performances and suggest optimized approaches for each task. Finally, we apply these methodologies to real-world Twitter data and present our findings through multiple graphs and statistical analyses. This study demonstrates the possibility of extracting new insights and novel information from unstructured textual Big Data in Politics.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-07-16book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/208677reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2086772026-06-13T07:49:27Z
dc.title.none.fl_str_mv Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
title Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
spellingShingle Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
Caliskan, Ufuk
Politics
Deep learning
Artificial intelligence
Big data
Statistics
Sentiment
title_short Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
title_full Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
title_fullStr Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
title_full_unstemmed Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
title_sort Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
dc.creator.none.fl_str_mv Caliskan, Ufuk
Pappagallo, Angela
Ortame, Francesco
Bruno, Mauro
Pugliese, Francesco
author Caliskan, Ufuk
author_facet Caliskan, Ufuk
Pappagallo, Angela
Ortame, Francesco
Bruno, Mauro
Pugliese, Francesco
author_role author
author2 Pappagallo, Angela
Ortame, Francesco
Bruno, Mauro
Pugliese, Francesco
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Politics
Deep learning
Artificial intelligence
Big data
Statistics
Sentiment
topic Politics
Deep learning
Artificial intelligence
Big data
Statistics
Sentiment
description [EN] Over the past decade, social media platforms have undergone significant and rapid expansion. One of the key challenges has been effectively analysing the vast amount of unstructured user-generated data they produce. This research delves into the analysis of Italian Twitter data through the application of advanced deep learning models across three primary objectives: text classification, sentiment analysis, and hate analysis. Five cutting-edge models are evaluated, each utilizing distinct word embeddings.Furthermore, this study investigates the effects of processing emojis and emoticons in Italian tweets on sentiment and hate analysis. We compare model performances and suggest optimized approaches for each task. Finally, we apply these methodologies to real-world Twitter data and present our findings through multiple graphs and statistical analyses. This study demonstrates the possibility of extracting new insights and novel information from unstructured textual Big Data in Politics.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-16
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/208677
url https://riunet.upv.es/handle/10251/208677
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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