Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
This addresses the challenge of analyzing complex sentiment in text for NLP applications, but it is incremental as it builds on existing classification methods.
The paper tackled the problem of sentiment classification in passages with conflicting tones, which reduces model performance, by introducing novel methodologies for isolating and aggregating sentiments, resulting in an MLP model that outperforms baselines across multiple datasets at a cost of ~1/100 of fine-tuning.
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.