A Unified Transformer-BiLSTM and Graph Attention Network Framework for Explainable Multilingual Opinion Mining and Relationship Inference in Complex Social-Citation Networks

Abstract

The paper takes into consideration the rising requirement for accurate mining of opinion and inferring relations among entities as the amount of multilingual online information increases rapidly. Thus, the paper seeks a uniform statistical learning methodology for processing multiple languages and exploiting valuable relational discoveries. The paper introduces an innovative model which combines transformer-based context embedding, BiLSTM for capturing of sentiment flows, and GAT for examining relational data. Transformers model cross-lingual context probability distribution, BiLSTM models temporal transitions in sentiment, whereas GAT offers an attention-based statistical weighting on data structured as a graph. Examples include social networks and citation graphs. The model yields a sentiment classification accuracy and macro F1-score of 92.4% over the four multilingual datasets (English, Spanish, and Hindi), surpasses all baseline methods. The model has achieved state-of-the-art result 86.4% in relation inference with graph attention mechanism, and shows excellent reliability and generalization. In this work, we propose a statistically sound and integrated framework which incorporates contextual, sequential and relational modelling of multilingual opinion mining. Graph structured learning integrated with probabilistic encoding and temporal modeling is a novel technique for such tasks, which also shows a promising direction in terms of scalability and generalizability across different domains.