BanglaPSG: A Bangla Public Service Grievance Dataset for Category and Severity Classification

Published: 21 July 2026| Version 1 | DOI: 10.17632/w6c4zx5t2s.1
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Description

This dataset, BanglaPSG, contains 6,105 manually annotated Bangla public service grievance texts collected from publicly available social media platforms in Bangladesh, including Facebook, YouTube, Instagram, Reddit, and other online sources. Each grievance is labeled with one of six public service categories (Food, Road & Transport, Water, Waste, Electricity, and Healthcare) and one of three severity levels (Low, Medium, High). The dataset was collected between December 2025 and June 2026, cleaned, anonymized, and annotated by native Bangla speakers following predefined annotation guidelines. BanglaPSG is designed to support research in Natural Language Processing (NLP), text classification, public grievance analysis, civic computing, and smart governance. It also includes benchmark-ready annotations for category and severity classification tasks, making it a valuable resource for developing and evaluating machine learning and deep learning models for low-resource Bangla language processing.

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Computer Science, Artificial Intelligence, Public Administration, Natural Language Processing, Machine Learning

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