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Open AccessArticle

Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

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Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain
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HiTZ Center—Ixa, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain
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Authors to whom correspondence should be addressed.
Academic Editor: Jacek Oskarbski
Sustainability 2021, 13(4), 2397; https://doi.org/10.3390/su13042397
Received: 31 December 2020 / Revised: 12 February 2021 / Accepted: 16 February 2021 / Published: 23 February 2021
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. View Full-Text
Keywords: sustainable transport; sentiment analysis; deep learning; information extraction; natural language processing sustainable transport; sentiment analysis; deep learning; information extraction; natural language processing
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MDPI and ACS Style

Serna, A.; Soroa, A.; Agerri, R. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability 2021, 13, 2397. https://doi.org/10.3390/su13042397

AMA Style

Serna A, Soroa A, Agerri R. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability. 2021; 13(4):2397. https://doi.org/10.3390/su13042397

Chicago/Turabian Style

Serna, Ainhoa; Soroa, Aitor; Agerri, Rodrigo. 2021. "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport" Sustainability 13, no. 4: 2397. https://doi.org/10.3390/su13042397

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