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    A Government Decision Analytics Framework Based on Citizen Opinion


    Rezk, Mohamed Adel and Khayat, Ghada A. El and Ojo, Adegboyega and Hussein, Safaa (2016) A Government Decision Analytics Framework Based on Citizen Opinion. In: Proceedings of the 9th International Conference on Theory and Practice of Electronic Governance. ACM Digital Library, pp. 27-30. ISBN 978-1-4503-3640-6

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    Abstract

    This ongoing research aims to develop a Government Decision Support Framework that employs citizen opinions and sentiments to predict the level of acceptance of newly proposed policies. The system relies on a knowledge base of citizen opinions and an Ontological Model comprising aspects and related terms of different policy domains as an input and a Bayesian predictive procedure. The work proceeds in four basic steps. The first step involves developing domain models comprising aspects for different policy domains in government and automatically acquiring semantically related terms for these aspects from associated policy documents. The second step involves computing citizen sentiments and opinions for the different policy aspects. The third involves updating the ontology with the computed sentiments and the last step involves employing a Bayesian Predictive Process to predict likely citizen opinion for a new proposal (policy) based on information available in the ontology. We provide some background to this work, describe our approach in some detail and discuss the progress made

    Item Type: Book Section
    Keywords: Government Decision Support; Decision Analytics; Bayesian Policy Acceptance Prediction; Citizen Satisfaction; Policy Aspects; Opinion Mining; Sentiment Analysis; Semantic Relatedness;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15817
    Identification Number: https://doi.org/10.1145/2910019.2910093
    Depositing User: Adegboyega Ojo
    Date Deposited: 12 Apr 2022 13:56
    Publisher: ACM Digital Library
    Refereed: Yes
    URI:

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