Aspect–Sentiment Triplet Extraction in Low-Resource Odia Language Using a Tri-Channel GCN

With the proliferation of digital platforms and smartphones, users increasingly express opinions in their native languages through online reviews. However, fine-grained sentiment analysis for such low-resource languages remains underexplored, as most Aspect-Based Sentiment Analysis (ABSA) methods target resource-rich languages such as English. To address this gap, we present Odia-TriGCN, a hybrid framework for Aspect Sentiment Triplet Extraction in the low-resource Odia language. It integrates tree-distance relations (dependency and constituent trees), syntactic dependencies, and biaffine semantic attention to capture rich structural and semantic cues. The model further leverages auxiliary features from an Odia news corpus and cross-lingual supervision using an Odia–English parallel corpus. We also incorporate a customized dependency parser trained on manually annotated data following the Universal Dependencies (UD) scheme, to obtain consistent head-dependent relations. Additionally, we extend the existing Odia ABSA dataset and introduce Odia-Triplet , a manually annotated dataset comprising aspect-opinion-sentiment triplets. Experiments across five domains show that Odia-TriGCN outperforms the strongest compared non-LLM baseline in each domain by 5.6 to 7.1 absolute F1 points. The framework is further evaluated in cross-lingual (Odia → Hindi, Odia → Bengali) and code-mixed (Odia–English, Odia–Hindi–English, Odia–Bengali–English) settings, providing further evidence of its applicability in multilingual contexts.