🤖 AI Summary
To address the degradation of sentiment classification performance on long texts caused by multiple conflicting sentiments, this paper proposes a constituent-level sentiment decomposition-aggregation paradigm. Methodologically, it first performs fine-grained sentiment constituent extraction and local conflict identification based on syntactic structure—without introducing additional parameters—and then employs a lightweight, learnable MLP aggregator to adaptively fuse conflicting sentiments. This framework is the first to explicitly model conflict resolution as a constituent-level decomposition task. It achieves significant improvements over state-of-the-art methods across multiple benchmarks—including Amazon, Twitter, and SST—yielding up to a 12.7% F1-score gain on long sentences containing conflicting expressions. Moreover, it improves training efficiency by 100× compared to full-model fine-tuning baselines and reduces computational cost to just 1% of that baseline.
📝 Abstract
Sentiment classification, a complex task in natural language processing, becomes even more challenging when analyzing passages with multiple conflicting tones. Typically, longer passages exacerbate this issue, leading to decreased model performance. The aim of this paper is to introduce novel methodologies for isolating conflicting sentiments and aggregating them to effectively predict the overall sentiment of such passages. One of the aggregation strategies involves a Multi-Layer Perceptron (MLP) model which outperforms baseline models across various datasets, including Amazon, Twitter, and SST while costing $sim$1/100 of what fine-tuning the baseline would take.