The aspect-based sentiment analysis (ABSA) consists of two subtasks: aspect term extraction (AE) and aspect term sentiment classification (ASC). Previous research on the AE task has not adequately leveraged syntactic information and has overlooked the issue of multi-word aspect terms in text. Current researchers tend to focus on one of the two subtasks, neglecting the connection between the AE and ASC tasks. Moreover, the problem of error propagation easily occurs between two independent subtasks when performing the complete ABSA task. To address these issues, we present a unified ABSA model based on syntactic features and interactive learning. The proposed model is called syntactic interactive learning based aspect term sentiment classification model (SIASC). To overcome the problem of extracting multi-word aspect terms, the model utilizes part-of-speech features, words features, and dependency features as textual information. Meanwhile, we designs a unified ABSA structure based on the end-to-end framework, reducing the impact of error propagation issues. Interaction learning in the model can establish a connection between the AE task and the ASC task. The information from interactive learning contributes to improving the model’s performance on the ASC task. We conducted an extensive array of experiments on the Laptop14, Restaurant14, and Twitter datasets. The experimental results show that the SIASC model achieved average accuracy of 84.11%, 86.65%, and 78.42% on the AE task, respectively. Acquiring average accuracy of 81.35%, 86.71% and 76.56% on the ASC task, respectively. The SIASC model demonstrates superior performance compared to the baseline model.

基于方面的情感分析（ABSA）由两个子任务组成：方面术语提取（AE）和方面术语情感分类（ASC）。先前关于 AE 任务的研究没有充分利用句法信息，并且忽略了文本中多词方面术语的问题。目前的研究人员倾向于关注两个子任务之一，而忽略了 AE 和 ASC 任务之间的联系。而且，在执行完整的ABSA任务时，两个独立的子任务之间很容易出现错误传播的问题。为了解决这些问题，我们提出了一个基于句法特征和交互式学习的统一 ABSA 模型。所提出的模型称为基于句法交互式学习的方面术语情感分类模型（SIASC）。为了克服提取多词方面术语的问题，该模型利用词性特征、单词特征和依存特征作为文本信息。同时，我们基于端到端框架设计了统一的ABSA结构，减少了错误传播问题的影响。模型中的交互学习可以在AE任务和ASC任务之间建立联系。来自交互式学习的信息有助于提高模型在 ASC 任务上的性能。我们对 Laptop14、Restaurant14 和 Twitter 数据集进行了广泛的实验。实验结果表明，SIASC模型在AE任务上的平均准确率分别为84.11%、86.65%和78.42%。在 ASC 任务上获得的平均准确率分别为 81.35%、86.71% 和 76.56%。与基线模型相比，SIASC 模型表现出卓越的性能。