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Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing

Kowalski, K; Esteve, M; Mikhaylov, SJ; (2021) Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing. Public Administration 10.1111/padm.12656. (In press). Green open access

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Abstract

Research on user satisfaction has increased substantially in recent years. To date, most studies have tested the significance of predefined factors thought to influence user satisfaction, with no scalable means of verifying the validity of their assumptions. Digital technology has created new methods of collecting user feedback where service users post comments. As topic models can analyse large volumes of feedback, they have been proposed as a feasible approach to aggregating user opinions. This novel approach has been applied to process reviews of primary care practices in England. Findings from an analysis of more than 200,000 reviews show that the quality of interactions with staff and bureaucratic exigencies are the key drivers of user satisfaction. In addition, patient satisfaction is strongly influenced by factors that are not measured by state‐of‐the‐art patient surveys. These results highlight the potential benefits of text mining and machine learning for public administration.

Type: Article
Title: Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/padm.12656
Publisher version: https://doi.org/10.1111/padm.12656
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Political Science
URI: https://discovery.ucl.ac.uk/id/eprint/10090118
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