Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport
dc.contributor.author | Serna Nocedal, Ainhoa | |
dc.contributor.author | Soroa Echave, Aitor ![]() | |
dc.contributor.author | Agerri Gascón, Rodrigo ![]() | |
dc.date.accessioned | 2021-03-05T11:06:24Z | |
dc.date.available | 2021-03-05T11:06:24Z | |
dc.date.issued | 2021-02-23 | |
dc.identifier.citation | Sustainability 13(4) : (2021) // Article ID 2397 | es_ES |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | http://hdl.handle.net/10810/50497 | |
dc.description.abstract | Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset. | es_ES |
dc.description.sponsorship | This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), Ayudas Fundación BBVA a Equipos de Investigación Científica 2018 (BigKnowledge), DeepText (KK-2020/00088), funded by the Basque Government and the COLAB19/19 project funded by the UPV/EHU. Rodrigo Agerri is also funded by the RYC-2017-23647 fellowship and acknowledges the donation of a Titan V GPU by the NVIDIA Corporation. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/RTI2018-096846-B-C21 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/RYC-2017-23647 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | sustainable transport | es_ES |
dc.subject | sentiment analysis | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | information extraction | es_ES |
dc.subject | natural language processing | es_ES |
dc.title | Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2021-02-26T14:51:58Z | |
dc.rights.holder | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |
dc.relation.publisherversion | https://www.mdpi.com/2071-1050/13/4/2397/htm | es_ES |
dc.identifier.doi | 10.3390/su13042397 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala |
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Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).