International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 18, No. 2, 8 Apr. 2026
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Empowering Community Work: Using Semi-Supervised Learning to Identify Emerging Community Needs and Service Gaps from Massive Unstructured Text
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Author(s)
Yih-Chang Chen
1,*
Index Terms
Semi-supervised Learning, Community Needs Assessment, Text Mining, Natural Language Processing, Service Deficiencies
Abstract
Community engagement is essential to social service delivery, yet traditional community needs assessment remains time-consuming and poorly suited for timely monitoring. This study proposes a semi-supervised learning framework to identify emerging community needs and service gaps from massive, mostly unlabeled, unstructured text. We construct an explicit heterogeneous text graph where each record is a document node linked to keyword and need-category nodes; document–document edges are built using a weighted combination of semantic similarity (BERT cosine), lexical overlap (keyword Jaccard), and temporal proximity. A graph neural network with iterative self-training leverages 3% expert-labeled seed data and the remaining unlabeled corpus to classify records into a 10-category need taxonomy. On 176,602 records, the proposed model achieves F1 = 0.895 and Recall = 0.899, outperforming supervised baselines trained on the same labeled ratio by 23.8% (macro-F1). Post-hoc quarterly aggregation of predictions enables trend monitoring and prioritization of service-gap severity for decision support.
Cite This Paper
Yih-Chang Chen, "Empowering Community Work: Using Semi-Supervised Learning to Identify Emerging Community Needs and Service Gaps from Massive Unstructured Text", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.2, pp.1-12, 2026. DOI:10.5815/ijisa.2026.02.01
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