Dual-Cell Recurrent Network for Target-Oriented Opinion Word Extraction on Global Fields

Target-oriented opinion word extraction (TOWE) is critical in aspect-based sentiment analysis. It aims at extracting opinion words that are related to aspect terms. Existing TOWE approaches primarily focused on explicit or implicit target aspects. However, few methods dealt with them simultaneously. For compensating this limitation, this study proposes a dual-cell recurrent network (DCRN) that combines aspect term extraction (ATE) and target-oriented opinion word extraction. The DCRN model is trained and evaluated on global fields, including explicit and implicit target aspects. Empirical results demonstrate that the proposed DCRN model outperforms existing methods by an average of 4.90% on the SemEva114–16 datasets. Furthermore, the DCRN model achieves higher Macro-F1 values than the IOG model on the Restaurant 14–16 datasets by 8.97%, 7.90 %, and 8.70 %, respectively. These results indicate that the DCRN model significantly improves the performance of TOWE and exhibits robust generalization capabilities.