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Qualitative Study on Social Media Sentiment Analysis for Evidence-Based Policy-Making in Electronic Voting in Nigeria: A Machine Learning Approach

Domain:

natural language processingsocioeconomic

Record type:

paper
Creator:
AfoOwoHamAka
Publisher:
Faculty of Education, Federal University Gusau
Host:avatar
Electronic voting (e-voting) has the potential to improve voter access, reduce logistical issues, and streamline elections. Despite these benefits, challenges related to public trust, security, and transparency hinder its adoption. Social media offers insights into public sentiment and perceptions about e-voting. This study uses social media sentiment analysis to inform evidence-based policymaking, helping to gauge public opinion and identify factors affecting e-voting acceptance. Through a machine learning framework, we analyze social media data to uncover sentiment patterns and their implications for policy. Key findings highlight public concerns regarding data security, voter privacy, and system reliability. Integrating these insights into policy recommendations provides a data-driven approach to address public concerns and foster trust in e-voting, contributing to the fields of sentiment analysis and e-governance and offering valuable guidance for developing secure, transparent, and publicly supported e-voting systems. Keyword: Electronic voting, sentiment analysis, machine learning, policy-making, social media

Visit

doi.orgzenodo.org

Tasks

sentiment analysistext classification

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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