Meta-Learning-Based Multi-Source Cross-Domain Fine-Grained Sentiment Classification with Domain Combination and Voting Ensemble

The rapid growth of textual data from social networks and online reviews has amplified the importance of research on cross-domain fine-grained sentiment classification. To address challenges such as data sparsity and domain discrepancies in current multi-source cross-domain sentiment classification (MSDFSC) methods, this study introduces a novel approach. This study proposes a multi-source cross-domain fine-grained sentiment classification method based on domain combination and voting ensemble from a meta-learning perspective (MLDCV-FGSC), the framework involves three stages. First, during data partitioning, we employ meta-learning by designating one of four domains as the target domain and the others as source domains. The data for each source domain are divided into support sets and query sets. The meta-training dataset is then constructed by combining the support sets from two source domains as the support set for a meta-task, while the query set from another source domain serves as the query set for the meta-task, thereby creating multiple meta-learning tasks. Secondly, during the meta-training stage, we train a base classifier for each meta-task employing a BERT model to encode both the text and the aspect terms. A multi-head attention mechanism is further incorporated to enhance the interaction modeling between them. The classifier parameters are updated through backpropagation to progressively improve its performance in cross-domain sentiment classification. Finally, in the meta-testing stage, we fine-tune the base classifiers to adapt to the target domain. The final prediction is obtained through an ensemble voting mechanism that integrates the output of the fine-tuned classifiers, ensuring robust generalization to the target domain. Our experimental validation, using datasets from various domains and various sentiment classification tasks, demonstrates that the proposed method achieves significant performance improvements in cross-domain fine-grained sentiment classification. Furthermore, multi-modal experiments conducted on other datasets further validate its effectiveness. These results underscore the efficiency and substantial potential of our approach in overcoming cross-domain sentiment classification challenges.