Aziz Kamran, Ji Donghong, Chakrabarti Prasun, Chakrabarti Tulika, Iqbal Muhammad Shahid, Abbasi Rashid
Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China.
Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, 313601, Rajasthan, India.
Sci Rep. 2024 Jun 25;14(1):14646. doi: 10.1038/s41598-024-61886-7.
Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model's interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.
基于方面的情感分析（ABSA）是一种细粒度的情感分析方法，旨在找出并评估文本中与特定方面相关的情感。ABSA包含一组子任务，这些子任务共同促进对多方面情感表达的详细理解。这些任务包括方面和观点词提取（ATE和OTE）、方面级情感分类（ALSC）、方面和观点词提取的耦合（AOE和AOPE），以及将这些元素具有挑战性地整合到情感三元组（ASTE）中。我们的研究引入了一个能够处理ABSA所有子任务的综合框架。该框架利用BERT的上下文优势进行细致的语言理解，并采用双仿射注意力机制精确描绘词关系。为了解决ABSA中固有的关系复杂性，我们纳入了一个多层增强图卷积网络（MLEGCN），该网络利用高级语言特征来完善模型的解释能力。我们还在MLEGCN中引入了一种系统的细化方法来增强词对表示，该方法利用方面和观点提取的隐含结果来确定词对的兼容性。我们在基准数据集上进行了广泛的实验，我们的模型在这些实验中显著优于现有方法。我们的贡献为情感分析建立了一个新范式，为从不同文本语料库中细致提取情感信息提供了一个强大工具。这项工作预计将对情感分析技术的发展产生重大影响，为广泛的应用提供有关消费者偏好和意见的更深入见解。
