Running summarizing tools on a new article

Abstract: Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of the text.While pre-trained language models (PLMs) have set the state-ofthe-art (SOTA) for ATSA, they are resourceintensive due to their large model sizes, restricting their wide applications to resourceconstrained scenarios.Conversely, conventional machine learning methods, such as Support Vector Machines (SVMs), offer the benefit of less resource requirement but have lower predictive accuracy.This paper introduces an innovative pipeline, termed SVM-ATSA, which bridges the gap between the accuracy of SVMbased methods and the efficiency of PLMbased methods.To improve the feature expression of SVMs and better adapt to the ATSA task, SVM-ATSA decomposes the learning problem into multiple view subproblems, and dynamically selects as well as constructs features with reinforcement learning.The experimental results demonstrate that SVM-ATSA surpasses SOTA PLM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters.