Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis
This work addresses aspect-based sentiment analysis for text analysis applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of aspect-based sentiment analysis by addressing the inadequate modeling of syntactic structures in traditional approaches, introducing a model that exploits syntactic knowledge and achieves superior performance on benchmark datasets.
Aspect-based sentiment analysis (ABSA), a nuanced task in text analysis, seeks to discern sentiment orientation linked to specific aspect terms in text. Traditional approaches often overlook or inadequately model the explicit syntactic structures of sentences, crucial for effective aspect term identification and sentiment determination. Addressing this gap, we introduce an innovative model: Syntactic Dependency Enhanced Multi-Task Interaction Architecture (SDEMTIA) for comprehensive ABSA. Our approach innovatively exploits syntactic knowledge (dependency relations and types) using a specialized Syntactic Dependency Embedded Interactive Network (SDEIN). We also incorporate a novel and efficient message-passing mechanism within a multi-task learning framework to bolster learning efficacy. Our extensive experiments on benchmark datasets showcase our model's superiority, significantly surpassing existing methods. Additionally, incorporating BERT as an auxiliary feature extractor further enhances our model's performance.