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.

翻译：基于方面的情感分析（ABSA）是一个细粒度的语言学问题，涉及从给定文本中提取多层面的方面、观点和情感。独立和复合的ABSA任务已被广泛用于文献中，以分析在线评论和社交媒体帖子中的细微信息。当前的ABSA方法通常依赖静态超参数进行注意力掩码机制，这在上下文适应性方面存在局限，可能忽略不同情境下词语的独特相关性。这导致在准确分析包含多个方面且情感不同的复杂句子时面临挑战。本文提出自适应掩码方法，根据上下文移除无关标记，以辅助ABSA中的方面术语提取和方面情感分类子任务。实验表明，所提方法在四个基准在线评论数据集上的准确率和F1分数均优于基线方法。此外，我们展示了所提方法可通过多种扩展进行应用，并通过示例文本的定性分析验证了所提方法在方面术语提取中的有效性。