An implicit aspect-based sentiment analysis method using supervised contrastive learning and knowledge embedding

Aspect-based sentiment analysis aims to analyze and understand people’s opinions from different aspects. Some comments do not contain explicit opinion words but still convey a clear human-perceived emotional orientation, which is known as implicit sentiment. Most previous research relies on contextual information from a text for implicit aspect-based sentiment analysis. However, little work has integrated external knowledge with contextual information. This paper proposes an implicit aspect-based sentiment analysis model combining supervised contrastive learning with knowledge-enhanced fine-tuning on BERT (BERT-SCL+KEFT). In the pre-training phase, the model utilizes supervised contrastive learning (SCL) on large-scale sentiment-annotated corpora to acquire sentiment knowledge. In the fine-tuning phase, the model uses a knowledge-enhanced fine-tuning (KEFT) method to capture explicit and implicit aspect-based sentiments. Specifically, the model utilizes knowledge embedding to embed external general knowledge information into textual entities by using knowledge graphs, enriching textual information. Finally, the model combines external knowledge and contextual features to predict the implicit sentiment in a text. The experimental results demonstrate that the proposed BERT-SCL+KEFT model outperforms other baselines on the general implicit sentiment analysis and implicit aspect-based sentiment analysis tasks. In addition, ablation experimental results show that the proposed BERT-SCL+KEFT model without the knowledge embedding module or supervised contrastive learning module significantly decreases performance, indicating the importance of these modules. All experiments validate that the proposed BERT-SCL+KEFT model effectively achieves implicit aspect-based sentiment classification.