MSRA-CCL: Multi-Source Reliability-Aware Aggregation and Cross-Domain Consistency Learning for Weakly Supervised Sentiment Classification

Abstract

Sentiment classification often relies on costly in-domain annotations, while weak-supervision sources provide low-cost but inconsistent labels. We propose Multi-Source Reliability-Aware Aggregation and Cross-domain Consistency Learning (MSRA-CCL) for weakly supervised sentiment classification. MSRA-CCL integrates four heterogeneous sources: VADER, TextBlob, a cross-domain TF–IDF classifier, and a cross-domain RoBERTa model. Their predictions, confidence, entropy, probability margins, and agreement are encoded into a 27-dimensional evidence representation. A reliability-aware gate learns instance-dependent soft targets from source-domain validation data without using target-domain training labels; however, labeled target-domain validation data are used only for student-checkpoint selection. The target-domain RoBERTa student is trained with confidence filtering, class balancing, soft-label supervision, and R-Drop consistency regularization. On TweetEval and DynaSent, MSRA-CCL achieves Macro-F1 scores of 0.6909 and 0.6893. The results demonstrate improved use of heterogeneous weak supervision.