How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis.

Lingyao Li, Yujie Mao, Yu Wang, Zihui Ma

Abstract read

In one paragraph

Article in Journal of air transport management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Decoding public sentiment topics in google map reviews on urban infrastructure development of belt and road initiative.Scientific reports · 2025
Article
Examining how customers perceive community pharmacies based on Google maps reviews: Multivariable and sentiment analysis.Exploratory research in clinical and social pharmacy · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lingyao LiDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.

Yujie MaoDepartment of Mechanical Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.

Yu WangDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.

Zihui MaDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Airport service quality (ASQ) is a competitive advantage for airport management in today's airport market. Since the COVID-19 health crisis has unprecedentedly influenced airport regulations and operations, effective measurement of ASQ has become crucial for airport administrations. Surveying travelers' attitudes is useful for ASQ assessment but collecting responses could be time-consuming and costly. Therefore, this paper adopts a data-driven crowdsourcing approach to study ASQ during the COVID-19 pandemic by investigating Google Maps reviews from the 98 busiest U.S. airports. To do so, this study develops a topical ontology of keywords regarding ASQ attributes and uses a sentiment tool to derive passengers' attitudes. Through sentiment analysis, Google Maps reviews show more positive sentiment toward

Indexed as

Airport service quality (ASQ)ASQ driven factorsCOVID-19CrowdsourcingGoogle maps reviewsSentiment analysis

Identifiers

PMID36101673

DOI10.1016/j.jairtraman.2022.102298

PMCPMC9458705

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.