A Self-Adaptive Agentic Mixture-of-Agent Families with Agent-to-Agent Communication for Automatic Sentiment Analysis

Automatic sentiment analysis requires models that can effectively handle texts of varying levels of complexity. Monolithic methods use the same algorithm uniformly, without taking into account the intrinsic complexity of the input text. In response to this need, we developed a Dynamic Agentic Mixture-of-Agents with Inter-Agent Communication and Adaptive Routing for Robust Sentiment Analysis (DAMA-Sent). This approach merges three distinct algorithmic paradigms: statistical learning, deep learning, and attention models. The decomposition process is carried out in a sophisticated system featuring a hierarchical routing system and an inter-agent communication system based on differentiable attention. Furthermore, each agent has a self-reflection module, a weighting mechanism that takes uncertainty into account and allows the agent to assess its own reliability. Finally, an adaptive early exit system halts processing as soon as an appropriate confidence threshold is reached or the computation budget is exhausted. In-depth analyses conducted on a corpus of tweets from American airlines reveal that the suggested approach can adjust to the intrinsic variability of textual complexity and surpasses static ensemble methods in terms of accuracy and computational cost, achieving an accuracy of 95.96%. Additional validations corroborate these trends, demonstrating both the structural relevance and the validity of our proposed framework.