A novel three-way based self-adaptive filtering model for sentiment analysis

Abstract

In the era of social media and diverse communication platforms, understanding human emotion across various modalities has become a crucial challenge. While significant progress has been made in feature extraction and interaction techniques, several unresolved issues persist, particularly concerning the balance between these two aspects. A central question is whether all extracted features are of equal importance, or if some may contain redundant or noisy information that undermines effective modality interaction. To address these challenges, we propose a novel Three-Way Decision-Based Self-Adaptive Filtering Model (TWSAFM). Inspired by the three-way decision (TWD) theory, we introduce a self-adaptive filtering module that categorizes extracted modal features into three distinct domains: acceptable, rejectable, and reconsidering. This classification allows for separate processing of features, enabling the model to prioritize essential information while minimizing the impact of redundant and noisy data. Experimental validation on three benchmark datasets demonstrates that TWSAFM outperforms state-of-the-art methods in sentiment analysis tasks. Furthermore, training studies and parameter sensitivity analysis underscore the effectiveness of TWSAFM in efficiently filtering out irrelevant and noisy features, highlighting its robust contribution to enhancing feature interaction.