Adaptive sentiment evaluation in social media analysis

Apró, Anikó and Sasi, Levente (2026) Adaptive sentiment evaluation in social media analysis. ANNALES MATHEMATICAE ET INFORMATICAE, 63. pp. 18-31. ISSN 1787-6117

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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-Triggered 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 largescale 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.

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