Aspect-Based Sentiment Analysis with Enhanced Opinion Tree Parsing and Parameter-Efficient Fine-Tuning for Edge AI

Abstract

Understanding user opinions from user comments or reviews in social media text mining is essential for marketing campaigns and many other applications. However, analyzing social media user comments presents significant challenges due to the complexity of discerning relationships between opinions and aspects, particularly when comments vary greatly in length. To effectively explore aspects and opinions in the sentences, techniques based on mining opinion sentiment of the referred aspects (implicitly or explicitly) in the user comments with ACOS (aspect-category-opinion-sentiment) quadruple extraction have been proposed. Among many others, the opinion tree parsing (OTP) scheme has been shown to be effective and efficient for the ACOS quadruple extraction task in aspect-based sentiment analysis (ABAS). In this study, we continue the efforts to design an efficient ABSA scheme. We extend the original OTP scheme further with richer context parsing rules, utilizing conjunctions and semantic modifiers to provide more context information in the sentence and thus effectively improving the accuracy of the analysis. Meanwhile, regarding the limitations of computation resources for edge devices in edge computing scenario, we also investigate the trade-off between computation saving (in terms of the percentage of model parameters to be updated) and the model's performance (in terms of inference accuracy) on the proposed scheme under PEFT (parameter-efficient fine-tuning). We evaluate the proposed scheme on publicly available ACOS datasets. Experiment results show that the proposed enhanced OTP (eOTP) model improves the OTP scheme both in precision and recall measurements on the public ACOS datasets. Meanwhile, in the design trade-off evaluation for resource-constrained devices, the experiment results show that, in model training, eOTP requires very limited parameters (less than 1%) to be retrained by keeping most of the parameters frozen (not modified) in the fine-tuning process, at the cost of a slight performance drop (around 4%) in F1-score compared with the case of full fine-tuning. These demonstrate that the proposed scheme is efficient and feasible for resource-constrained scenarios such as for mobile edge/fog computing services.