TMAK -Plus at SIGHAN -2024 dim ABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis (2024)

Abstract: The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.

## introduction we consider the dimabsa task a challenging sentiment analysis problem @xcite that requires a deep understanding of natural language and the ability to sense sentiments with distinct character traits. specifically, aspect detection, opinion detection, and aspect classification account for distinct sentiment analysis abilities, while intensity estimation requires a comprehensive understanding of valence and arousal intensities corresponding to the empathetic sentiment sensitivity of different individuals. to address the dimabsa task, we propose a mac model that sequentially solves the aforementioned challenges, as shown in figure 1 . the model consists of gpt-4o (gpt-4o-2024-05-13) powered generative neural ai agents, including ad, od, cd, ac, oc, cc, and ie. we pair ad and ac for collaborative aspect detection, od and oc for collaborative opinion detection, and cd and cc for collaborative aspect classification. @xmath0: {"s": hsi, "r": [{"a": hai, "c": hci, "o": hoi, "i": hvi#hai}]} averages the sentiment intensities from 8 estimators with distinct character traits. all agents exchange messages in json format, including the detected results or critical feedback, in a symbolic manner to ensure transparency and rationality. unlike previous approaches that focus on the end-to-end training of a single neural network with a particular training set @xcite , the mac model is powered by multiple generative ai agents. these agents require only a few training examples and clear instructions to perform their tasks in a more robust manner and generalize easily to out-of-domain data. our results demonstrate that mac provides a generalizable and transparent neuro-symbolic ai framework for solving key phrase detection, multi-class classification, and regression tasks that require a deep understanding of natural language. the main contributions are as follows: ## conclusion in this paper, we proposed a mac model for the dimabsa task, representing a new paradigm of neuro-symbolic ai. our approach employs multiple generative ai agents, each specializing in different subtasks, ensuring a robust and transparent workflow. the mac model operates in a transparent and rational manner, demonstrated by its accurate identification of aspects, categories, opinions, and sentiment intensities. by incorporating agents with distinct character traits, we mimic the sentiment receptions of different individuals, enhancing the model's effectiveness. additionally, the use of generative ai agents with few-shot learning enables mac to easily generalize to out-ofdomain data. future work will focus on addressing the hallucination problem within the neuro-symbolic ai framework, improving reliability and consistency of outputs, and extending the model's applicability to other domains.