Adaptive sentiment evaluation in social media analysis
TL;DR
The results show that ADRTW remains competitive with static averaging in controlled settings while preserving context-sensitive local variation in large-scale discourse analysis, and the proposed framework provides a transparent and reusable basis for examining emotional dynamics in social media discourse.
Abstract
. Social media sentiment analysis faces a persistent aggregation problem: lexicon-based and transformer-based models often produce inconsistent outputs for the same short, informal, and stylistically heterogeneous texts. This paper introduces ADRTW (Adaptive Dynamic Reliability-Trig-gered Weighting), an interpretable sentiment fusion framework that combines heterogeneous sentiment estimators using rule-guided reliability weights derived from textual cues, inter-model disagreement, and consistency patterns [5, 8]. The framework is evaluated on a Reddit dataset containing 1,577 posts, 354,050 comments, and 187,666 authors collected between 2017 and 2025, together with a controlled synthetic benchmark for aggregation comparison. The results show that ADRTW remains competitive with static averaging in controlled settings while preserving context-sensitive local variation in large-scale discourse analysis. Beyond sentiment fusion, the ADRTW-derived signal supports complementary analyses of online discussions, including temporal trend inspection, toxicity-aware interpretation, and participation-based clustering. Overall, the proposed framework provides a transparent and reusable basis for examining emotional dynamics in social media discourse.