Aug 2026· Knowledge and Information Systems· Vol 68· 0 citations· 62 references
TL;DR
Results demonstrate that jointly incorporating structural, semantic, and sentiment information yields more coherent, semantically aligned communities than structure-only baselines while maintaining practical scalability.
The proposed model, Crossbred Analogous Tree-Knowledge Deep (CAT-KD), is composed of five key components for comprehensive sentiment analysis and achieves an accuracy of 94.83% and an F1-score of 94.12%, outperforming baseline models with improvements of up to 0.62% in accuracy and 2.46% in F1-score on Twitter datasets...
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.
Aniko Apro, L. Sasi· Annales Mathematicae et Info...· 0 citations
The proposed framework provides a rigorous, interpretable and uncertainty-aware solution for social media influence-domain identification and offers practical applications in influencer marketing, digital communication and reputation management.
Fatima-Zahrae Sifi, Wafae Sabbar, A. Mzabi· Journal of Intelligent Decis...· 0 citations
The proposed framework provides an interpretable approach for mapping inter-community sentiment dependencies from social media time series, and shows that collective online sentiment is well described by structured inter-community averaging, yielding interpretable, empirically grounded influence graphs.
Yulong He, A. Proskurnikov, Artem Sedakov· Social Network Analysis and...· 3 citations
A proposed Multi-View Graph Attention Network (MV-GAT) that improves political topic classification by integrating three complementary graph representations: a semantic content graph, a user interaction graph, and a temporal propagation graph.
Dhea Sila Mukti, Rizal Tjut Adek, Cut Agusniar· Brilliance Research of Artif...· 0 citations
This work proposes the SENSE model, a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction, and employs a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained prefer...
Ying-Jie Chen, Xiang Li, Dong Chen et al.· Proceedings of the Thirty-Fi...· 0 citations
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