Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

Ainhoa Serna, Aitor Soroa and Rodrigo Agerri
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Ainhoa Serna: Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain
Aitor Soroa: HiTZ Center—Ixa, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain
Rodrigo Agerri: HiTZ Center—Ixa, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain

Sustainability, 2021, vol. 13, issue 4, 1-19

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.

Keywords: sustainable transport; sentiment analysis; deep learning; information extraction; natural language processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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