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In the current digital age, government bodies
and public institutions are flooded with a significant number
of complaints from citizens via online platforms. These
complaints can vary from small civic matters to severe
emergencies like accidents, fire safety, or public security risks.
However, most of the current complaint handling systems
process complaints on a first-come, first-served basis without
considering their urgency or significance. This has resulted in
a delay in the processing of critical complaints while less
important ones are dealt with first.The Smart Complaint
Prioritization System aims to overcome this problem by
automatically evaluating the complaint content and allocating
priority levels based on their severity. The system employs
Natural Language Processing (NLP) and Machine Learning
algorithms to scan keywords, categories, and semantic
information in the complaint description. According to this
evaluation, complaints are categorized as High, Medium, or
Low priority, giving utmost importance to critical complaints.
The proposed system automatically prioritizes complaints,
thus decreasing the manual effort required, increasing
response time, increasing transparency, and facilitating
informed decision-making on e-governance platforms. In
summary, the proposed solution offers a systematic and
intelligent way of efficiently and effectively handling a
significant number of public complaints.
Keywords:
E-Governance, Complaint Management System, Complaint Prioritization, Natural Language Processing (NLP), Machine Learning, Text Classification, Decision Support System, Public Grievance Redressal, Automated Priority Assignment, Digital Governance
Cite Article:
"Smart Complaint Prioritization System using NLP and Machine Learning for E-Governance ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a642-a647, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604091.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator