Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

Ioannis Politis, Georgios Georgiadis (), Aristomenis Kopsacheilis, Anastasia Nikolaidou and Panagiotis Papaioannou
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Ioannis Politis: Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Aristomenis Kopsacheilis: Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Anastasia Nikolaidou: Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Panagiotis Papaioannou: Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Sustainability, 2021, vol. 13, issue 23, 1-19

Abstract: The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

Keywords: COVID-19; Twitter; sentiment analysis; public transport; latent Dirichlet allocation (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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