Abstract
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.
Abstract (translated)
面向方面的情感分析（ASSA）是一个细粒度的语言学问题，旨在从给定文本中提取多方面的内容、意见和情感。离散和组合ASSA任务在文献中得到了广泛应用，以研究在线评论和社交媒体帖子中存在的微妙的上下文信息。当前的ASSA方法通常依赖于静态超参数的注意力遮蔽机制，这可能会在上下文适应方面遇到困难，并可能忽视不同情况中单词的独特相关性。这导致在分析复杂句子中多个方面情感存在差异时存在挑战。在本文中，我们提出了适应性遮蔽方法，根据上下文删除无关词，以协助进行ASSA的方面词提取和情感分类子任务。我们通过实验证明，与基线方法相比，所提出的方法在四个基准在线评论数据集上的准确性和F1分数方面具有优势。此外，我们还证明了所提出的方法可以通过多个自定义进行扩展，并且通过使用样本文本进行方面词提取的定性分析展示了所提出方法的有效性。
URL
https://arxiv.org/abs/2402.13722
