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User profiling and satisfaction inference in public information access services

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Abstract

Public information access services are provided by dozens of countries around the world as a means to promote transparency and democracy, and present a number of research opportunities for the development of computational models that help understand both users and their needs. Based on these observations, the present work discusses how the use of Natural Language Processing (NLP) methods may harvest valuable knowledge about citizen-government communication in user profiling and satisfaction inference tasks. More specifically, from a large text dataset of this kind, we build a number of models using a range of supervised machine learning methods - including bidirectional long short-term memory networks (LSTMs), pre-trained context-sensitive embeddings (BERT) and others - and show that these outperform textual and non-textual baseline alternatives alike. This outcome makes a case in favour of NLP methods for these tasks, and paves the way for further applications in the public information access domain.

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Availability of Data and Material

https://doi.org/https://drive.google.com/file/d/12sFdgipuK2d1QyrTlnv5QwFj1Gs5mdnI/view?usp=sharing

Code Availability

https://github.com/arthurmarcal/user-satisfaction-public-services

Notes

  1. https://www.foia.gov/

  2. http://esic.gov.br/

  3. Supported by the Brazilian access information legislation and decree 8777/16.

  4. https://esic.cgu.gov.br/

  5. For a comparison between content-based (e.g., sentiment) and response time features in online chat customer satisfaction, see also (Park et al., 2015).

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Acknowledgements

The authors are grateful to Dr Sidney Evaldo Leal and Dr Sandra Maria Aluísio (USP) for the Coh-Metrix-Port feature extraction, and to Dr Elias Jacob de Menezes Neto (UFRN) for providing us with an early version of the present corpus. We also thank the anonymous reviewers for the valuable input to improve this article.

Funding

The third author received support from the University of São Paulo PRP grant nr. 668/2018.

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  1. University of São Paulo, Av Arlindo Bettio 1000, São Paulo, Brazil

    Arthur Marçal Flores, Matheus Camasmie Pavan & Ivandré Paraboni

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  1. Arthur Marçal Flores
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Contributions

Arthur Flores: Conceptualisation, Methodology, Software, Validation, Formal analysis, Investigation, Writing - original draft, reviewing and editing.

Matheus Pavan: User profiling methodology, software, and validation.

Ivandré Paraboni: Conceptualisation, Writing - review and editing, supervision, project, fund acquisition.

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Correspondence to Ivandré Paraboni.

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Flores, A.M., Pavan, M.C. & Paraboni, I. User profiling and satisfaction inference in public information access services. J Intell Inf Syst 58, 67–89 (2022). https://doi.org/10.1007/s10844-021-00661-w

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