Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis
This work addresses the challenge of accurately analyzing complex sentences in online reviews and social media for ABSA, though it appears incremental as it builds on existing masking techniques with adaptive improvements.
The paper tackled the problem of Aspect-Based Sentiment Analysis (ABSA) by addressing the limitations of static attention-masking mechanisms, which struggle with context adaptation and handling multiple aspects with differing sentiments. The result was that the proposed adaptive masking methods outperformed baseline methods in accuracy and F1 scores on four benchmark online review datasets.
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem that entails the extraction of multifaceted aspects, opinions, and sentiments from the given text. Both standalone and compound ABSA tasks have been extensively used in the literature to examine the nuanced information present in online reviews and social media posts. Current ABSA methods often rely on static hyperparameters for attention-masking mechanisms, which can struggle with context adaptation and may overlook the unique relevance of words in varied situations. This leads to challenges in accurately analyzing complex sentences containing multiple aspects with differing sentiments. In this work, we present adaptive masking methods that remove irrelevant tokens based on context to assist in Aspect Term Extraction and Aspect Sentiment Classification subtasks of ABSA. We show with our experiments that the proposed methods outperform the baseline methods in terms of accuracy and F1 scores on four benchmark online review datasets. Further, we show that the proposed methods can be extended with multiple adaptations and demonstrate a qualitative analysis of the proposed approach using sample text for aspect term extraction.