IAF-LG: An Interactive Attention Fusion Network With Local and Global Perspective for Aspect-Based Sentiment Analysis

One of the interesting trending phenomena in sentiment analysis is the prediction of sentiment given by the user towards an aspect term. Till today, a considerable number of researchers have proposed varying methodologies for predicting aspect-based sentiments. But they mostly encapsulate the semantic information by manifesting themselves within a local boundary around each aspect term and overlook capturing the semantic concept that is conveyed within the entire review (global). Therefore, this study proposes a model, IAF-LG, that performs semantic learning at both local and global scales to discover aspect-based sentiments. IAF-LG first encodes the local semantics by fusing contextual-semantic dependencies between tokens and computing relational semantics between inter-aspects. Next, it develops the global semantics by formulating interactions between local semantics and review-based sentiment learning. Lastly, it conjoins the local and global interactive learning to earn credible semantics for predicting the accurate sentiment of aspect terms. Extensive experiments on publicly available datasets demonstrate the significantly improved performance of IAF-LG than competitive baselines.