A syntactic features and interactive learning model for aspect-based sentiment analysis

Abstract

Abstract 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.

Keywords

• Error propagation
• Multi-word aspect terms
• Dependency features
• Interactive learning