Electronics 2024, 13(18), 3724; https://doi.org/10.3390/electronics13183724 (registering DOI) - 19 Sep 2024
Abstract
Dimensional aspect-based sentiment analysis (dimABSA) aims to recognize aspect-level quadruples from reviews, offering a fine-grained sentiment description for user opinions. A quadruple consists of aspect, category, opinion, and sentiment intensity, which is represented using continuous real-valued scores in the valence–arousal dimensions. To address
[...] Read more.
Dimensional aspect-based sentiment analysis (dimABSA) aims to recognize aspect-level quadruples from reviews, offering a fine-grained sentiment description for user opinions. A quadruple consists of aspect, category, opinion, and sentiment intensity, which is represented using continuous real-valued scores in the valence–arousal dimensions. To address this task, we propose a hybrid approach that integrates the BERT model with a large language model (LLM). Firstly, we develop both the BERT-based and LLM-based methods for dimABSA. The BERT-based method employs a pipeline approach, while the LLM-based method transforms the dimABSA task into a text generation task. Secondly, we evaluate their performance in entity extraction, relation classification, and intensity prediction to determine their advantages. Finally, we devise a hybrid approach to fully utilize their advantages across different scenarios. Experiments demonstrate that the hybrid approach outperforms BERT-based and LLM-based methods, achieving state-of-the-art performance with an F1-score of 41.7% on the quadruple extraction.
Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
